Dr Ying Wang

Tubular structures are widely used in infrastructure, especially for pipes and marine installations. Corrosion is a key damage type for these structures, and if undetected may cause structural deficiency or even collapse. This paper investigates the effects of localised corrosion on the vibration properties of axisymmetric tubular structures and proposes a novel baseline-free damage identification method to overcome the fact that the difference between undamaged and damaged properties is usually small and contaminated by noise. The proposed vibration-based method exploits the phenomenon of repeated mode pairs which is peculiar to axisymmetric tubular structures with non-axisymmetric damage. Unlike intact tubular structures, it is found that each vibration mode of a locally damaged tubular structure comprises of two components: active and passive. The active component is sensitive to the local damage and can be used to infer damage existence while the passive component is retentive of the undamaged pipe's properties. Therefore, the passive component of a damaged pipe’s local vibration mode pair (LVMP) can be used to predict its baseline (undamaged) modal properties, which in this research was achieved within a margin of ±2%, whereas the active component confirms damage existence. The work also establishes the criteria for validation of LVMPs through experimental tests involving various corrosion scenarios ranging from longitudinal strips to through-thickness pits. Based on the obtained results, it is proposed that the properties of LVMPs can be linked to corrosion-like local damage in axisymmetric tubular structures, underpinning a new damage detection and identification approach.

The offshore wind turbines (OWTs) are dynamically sensitive, whose fundamental frequency can be very close to the forcing frequencies activated by the environmental and turbine loads. Minor changes of support conditions may lead to the shift of natural frequencies, and this could be disastrous if resonance happens. To monitor the support conditions and thus to enhance the safety of OWTs, a model updating method is developed in this study. A hybrid sensing system was fabricated and set up in the laboratory to investigate the long-term dynamic behaviour of the OWT system with monopile foundation in sandy deposits. A finite element (FE) model was constructed to simulate structural behaviours of the OWT system. Distributed nonlinear springs and a roller boundary condition are used to model the soil-structure-interaction (SSI) properties. The FE model and the test results were used to analyze the variation of the support condition of the monopile, through an FE model updating process using Estimation of Distribution Algorithms (EDAs). The results show that the fundamental frequency of the test model increases after a period under cyclic loading, which is attributed to the compaction of the surrounding sand instead of local damage of the structure. The hybrid sensing system is reliable to detect both the acceleration and strain responses of the OWT model and can be potentially applied to the remote monitoring of real OWTs. The EDAs based model updating technique is demonstrated to be successful for the support condition monitoring of the OWT system, which is potentially useful for other model updating and condition monitoring applications.

Monitoring-enhanced resilience in transport management is emerging together with the new technologies and digital data, however have not been fully explored yet. Digital technologies have the potential to provide rapid resilience assessments in a quantifiable and engineered manner for transport infrastructure, which is exposed to multiple natural and human-induced hazards and diverse loads throughout their life-cycle. Physical damage and disruption of networks and interdependent systems may cause tremendous socioeconomic impact, affecting world economies and societies. Nowadays, transport infrastructure stakeholders have shifted the requirements in risk and resilience assessment. The expectation is that risk is estimated efficiently, almost in real-time with high accuracy, aiming at maximising the functionality and minimising losses. Nevertheless, no integrated framework exists for quantifying resilience to diverse hazards, based on structural and functionality monitoring (SHFM) data, and this is the main capability gap that this paper envisages filling. Monitoring systems have been used widely in transport infrastructure and have been studied extensively in the literature. Data can facilitate prognosis of the asset condition and the functionality of the network, informing computer-based asset and traffic models, which can assist in defining actionable performance indicators, for diagnosis and for defining risk and loss expediently and accurately. Evidence exists that SHFM is an enabler of resilience. However, strategies are absent in support of monitoring-based resilience assessment in transport infrastructure management. In response to the above challenge, this paper puts forward for the first time in the international literature, a roadmap for monitoring-based quantification of resilience for transport infrastructure, based on a comprehensive review of the current state-of-the-art. It is a holistic asset management roadmap, which identifies the interactions among the design, monitoring, risk assessment and quantification of resilience to multiple hazards. Monitoring is embraced as a vital component, providing expedient feedback for recovery measures, accelerating decision-making for adaptation of changing ecosystems and built environments, utilising emerging technologies, to continuously deliver safer and resilient transport infrastructure.

Offshore wind turbines (OWTs) have emerged as a reliable source of renewable energy, witnessing massive deployment across the world. While there is a wide range of support foundations for these structures, the monopile and jacket are most utilised so far; their deployment is largely informed by water depths and turbine ratings. However, the recommended water depth ranges are often violated, leading to cross-deployment of the two foundation types. This study firstly investigates the dynamic implication of this practice to incorporate the findings into future analysis and design of these structures. Detailed finite element (FE) models of Monopile and Jacket supported offshore wind turbines are developed in the commercial software, ANSYS. Nonlinear Soil springs are used to simulate the soil-structure interactions (SSI) and the group effects of the jacket piles are considered by using the relevant modification factors. Modal analyses of the fixed and flexible-base cases are carried out, and natural frequencies are chosen as the comparison parameters throughout the study. Secondly, this study constructs a few-parameters SSI model for the two FE models developed above, which aims to use fewer variables in the FE model updating process without compromising its simulation quality. Maximum lateral soil resistance and soil depths are related using polynomial equations, this replaces the standard nonlinear soil spring model. The numerical results show that for the same turbine rating and total height, Jacket supported OWTs generally have higher first-order natural frequencies than Monopile supported OWTs, while the reverse is true for the second-order vibration modes, for both fixed and flexible foundations. This contributes to future design considerations of OWTs. On the other hand, with only two parameters, the proposed SSI model has achieved the same accuracy as that using the standard model with seven parameters. It has the potential to become a new SSI model, especially for the identification of soil properties through the model updating process.

Offshore Wind Turbines are a complex, dynamically sensitive structure owing to their irregular mass and stiffness distribution and complexity of the loading conditions they need to withstand. There are other challenges in particular locations such as typhoon, hurricane, earthquake, sea-bed current, tsunami etc. As offshore wind turbines have stringent Serviceability Limit State (SLS) requirements and need to be installed in variable, and often complex ground conditions, their foundation design is challenging. Foundation design must be robust due to the enormous cost of retrofitting in a challenging environment should any problem occurs during the design lifetime. Traditionally, engineers use conventional types of foundation system such shallow Gravity-Based Foundations (GBF), suction caissons or slender pile or monopile owing to prior experience with designing such foundations for the oil and gas industry. For offshore wind turbine, however, new types of foundations are being considered for which neither prior experience nor guidelines exist. One of the major challenges is to develop a method to de-risk the life cycle of offshore wind turbines in diverse met-ocean and geological conditions. The paper, therefore, has the following aims: (a) Provide an overview of the complexities and the common SLS performance requirements for offshore wind turbine; (b) Discuss the use of physical modelling for verification and validation of innovative design concepts, taking into account all possible angles to de-risk the project. (c) Provide examples on applications of scaled model tests.

Anion-exchange membranes (AEM) containing saturated-heterocyclic benzyl-quaternary ammonium (QA) groups synthesised by radiation-grafting onto poly(ethylene-co-tetrafluoroethylene) (ETFE) films are reported. The relative properties of these AEMs are compared with the benchmark radiation-grafted ETFE-g-poly(vinylbenzyltrimethylammonium) AEM. Two AEMs containing heterocyclic-QA head groups were down-selected with higher relative stabilities in aqueous KOH (1 mol dm-3) at 80°C (compared to the benchmark): these 100 μm thick (fully hydrated) ETFE-g-poly(vinylbenzyl-Nmethylpiperidinium)- and ETFE-g-poly(vinylbenzyl-N-methylpyrrolidinium)-based AEMs had as-synthesised ion-exchange capacities (IEC) of 1.64 and 1.66 mmol g-1, respectively, which reduced to 1.36 mmol dm-3 (ca. 17 – 18% loss of IEC) after alkali ageing (the benchmark AEM showed 30% loss of IEC under the same conditions). These down-selected AEMs exhibited as-synthesised Cl- ion conductivities of 49 and 52 mS cm-1, respectively, at 90°C in a 95% relative humidity atmosphere, while the OH- forms exhibited conductivities of 138 and 159 mS cm-1, respectively, at 80°C in a 95% relative humidity atmosphere. The ETFE-g-poly(vinylbenzyl-N-methylpyrrolidinium)-based AEM produced the highest performances when tested as catalyst coated membranes in H2/O2 alkaline polymer electrolyte fuel cells at 60°C with PtRu/C anodes, Pt/C cathodes, and a polysulfone ionomer: the 100 μm thick variant (synthesised from 50 μm thick ETFE) yielded peak power densities of 800 and 630 mW cm-2 (with and without 0.1 MPa back pressurisation, respectively), while a 52 μm thick variant (synthesised from 25 μm thick ETFE) yielded 980 and 800 mW cm-2 under the same conditions. From these results, we make the recommendation that developers of AEMs, especially pendent benzyl-QA types, should consider the benzyl-Nmethylpyrrolidinium head-group as an improvement to the current de facto benchmark benzyltrimethylammonium headgroup.

It is known that rock masses are inhomogeneous, discontinuous media composed of rock material and naturally occurring discontinuities such as joints, fractures and bedding planes. These features make any analysis very difficult using simple theoretical solutions. Generally speaking, back analysis technique can be used to capture some implicit parameters for geotechnical problems. In order to perform back analyses, the procedure of trial and error is generally required. However, it would be time-consuming. This study aims at applying a neural network to do the back analysis for rock slope failures. The neural network tool will be trained by using the solutions of finite element upper and lower bound limit analysis methods. Therefore, the uncertain parameter can be obtained, particularly for rock mass disturbance

Among many structural health monitoring (SHM) methods, guided wave (GW) based method has been found as an effective and efficient way to detect incipient damages. In comparison with other widely used SHM methods, it can propagate in a relatively long range and be sensitive to small damages. Proper use of this technique requires good knowledge of the effects of damage on the wave characteristics. This needs accurate and computationally efficient modeling of guide wave propagation in structures. A number of different numerical computational techniques have been developed for the analysis of wave propagation in a structure. Among them, Spectral Element Method (SEM) has been proposed as an efficient simulation technique. This paper will focus on the application of GW method and SEM in structural health monitoring. The GW experiments on several typical structures will be introduced first. Then, the modeling techniques by using SEM are discussed.

The effect of mega braces on structural stiffne ss has been comprehensivel y discussed for various mega-braced frame-core tube structures. However, few studies have considered how mega braces affect the failure mechanism of mega-structures exposed to seismic action, which is a nonlinear process. To address this issue, we present a study on the effects of diff erent brace patterns on the failure mechanism and seismic performance of mega-braced frame-core tube structures. Specifically, the yield order of components, the distribution of plasticity, the distribution of internal forces, the degradation of structural nonlinear stiffness, and the behavior factor have been investigated. This study reveals that the yield of mega braces will change the deformation mode of adjacent mega columns, and thus affect the plasticity distribution of adjacent sub-structures. The enhancement of mega braces improves the exterior tubes (thereby increasing their capacity to serve as the second line of seismic defence), mitigates the rate at which system stiffness degrades, and improves the overstrength of the structural systems. In addition, after mega braces yield, the maintenance of a higher-amplitude axial force changes the proportion of internal force components in mega columns, reducing their ductility and further affecting the overall ductility of the structural system.

Absolute stress in structural steel members is an important parameter for the design, construction, and servicing of steel structures. However, it is difficult to measure via traditional approaches to structural health monitoring. The ultrasonic time-of-flight (TOF) method has been widely studied for monitoring absolute stress by measuring the change of ultrasonic propagation time induced by stress. The TOF of the two separated shear-wave modes induced by birefringence, which is particular to shear waves, is also affected by stress to different degrees. Their synthesis signal amplitude spectrum exhibits a minimum that varies with stress, which makes it a potential approach to evaluating uniaxial stress using the shear-wave amplitude spectrum. In this study, the effect of steel-member stress on the shear-wave amplitude spectrum from the interference of two shear waves produced by birefringence is investigated, and a method of uniaxial absolute stress measurement using shear-wave spectral analysis is proposed. Specifically, a theoretical expression is derived for the shear-wave pulse-echo amplitude spectrum, leading to a formula for evaluating uniaxial absolute stress. Three steel-member specimens are employed to investigate the influence of uniaxial stress on the shear-wave pulse-echo amplitude spectrum. The testing results indicate that the amplitude spectrum changes with stress, and that the inverse of the first characteristic frequency in the amplitude spectrum and its corresponding stress exhibit a near-perfect linear relationship. On this basis, the uniaxial absolute stress of steel members loaded by a test machine is measured by the proposed method. Parametric studies are further performed on three groups of steel members made of 65# steel and Q235 steel to investigate the factors that influence the testing results. The results show that the proposed method can measure and monitor steel-members uniaxial absolute stress on the laboratory scale and has potential to be used in practical engineering with specific calibration.

The absolute stress in the in-serviced steel members is a critical indicator employed for the evaluation of structural performance. In the field of structural health monitoring, the stress is usually monitored by the stress monitoring system. However, the monitored stress is the relative value, rather than the absolute value. The longitudinal critically refracted (Lcr) wave has shown potential for use in absolute stress measurement. The accurate measurement of the Lcr wave time-of-flight (TOF) is the core issue with this method. In this study, a cross-correlation-based algorithm is presented for stress evaluation using the Lcr wave. Specifically, a cross-correlation theoretical formula is derived and a five-step framework is proposed for the Lcr wave TOF measurement. Four steel members are employed to investigate the parametric calibration using the Lcr wave to measure the stress. On this basis, the proposed cross-correlation-based algorithm is used to evaluate the stress of a steel member. The results indicate that the cross-correlation-based algorithm can measure the Lcr wave TOF without filtering the noise signal, and the stress measurement results are better than those of the traditional peak value method. The proposed method provides a potential way to measure the absolute stress in practical engineering applications.

The various elements that are affecting the Earth's climate have brought climate change to the top of the priority list amongst scientists and policy-makers. Expected changes to local climatic conditions impact directly on the surrounding environment and potentially lead to changes in the degradation processes of building materials, affecting the durability and service life of infrastructures. The aim of this paper is to investigate the effects of future climate projections on concrete structures in Malta, in particular on carbonation-induced corrosion resulting from increasing temperatures and CO2 concentrations. Thirteen reinforced concrete structures in Malta were chosen for a retrospective analysis in order to validate two carbonation depth prediction models. The validated prediction models were subsequently used to evaluate the varying climate change scenarios in order to determine the effects on concrete carbonation depth for several concrete grades. The age of the structures used for the retrospective analysis ranged from 10 to 60 years. The field data verified the validity of both prediction models for structures with carbonation depths less than 50mm. Although both models proved valid for the retrospective analysis, a difference was noted between the models with regards to the predicted carbonation depth in relation to different climatic scenarios. An increase in carbonation depth of up to 40% is being predicted, by 2070, when considering the worst case climatic scenario. The findings prove that climate change plays a major role on the carbonation depth of concrete, which in turn reduces the service life of concrete structures.

Active mass damper/driver (AMD) control system has been proposed as an effective tool for high-rise buildings to resist strong dynamic loads. However, such disadvantage as time-varying delay in AMD control systems impedes their application in practices. Time-varying delay, which has an effect on the performance and stability of single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems, is considered in the paper. In addition, a new time-delay compensation controller based on regional pole-assignment method is presented. To verify its effectiveness, the proposed method is applied to a numerical example of a ten-storey frame and an experiment of a single span four-storey steel frame. Both numerical and experimental results demonstrate that the proposed method can enhance the performances of an AMD control system with time-varying delays.

The absolute stress of steel members is a key parameter for determining the performance of steel structures. Compared with other non-destructive evaluation methods, ultrasonic methods, which correlate material stress with ultrasonic velocity, have received the greatest amount of research attention. In this study, we investigated the measurement of the absolute stress distribution of steel members using two ultrasonic methods: a longitudinal critically refracted (Lcr) wave method and a shear wave method. The Lcr wave is generated from the longitudinal wave mode conversion and exhibits the greatest sensitivity to stress. The shear waves are generated by the birefringence effect, and their synthesis signal spectrum exhibits a minimum that varies with stress. A comparison of the two absolute stress evaluation methods is performed. Specifically, four steel members with identical dimensions and materials are used to investigate the discreteness of the calibrated parameters. The uniaxial absolute stress distributions of two steel members with variable cross-sections are measured using the two methods and verified using the traditional strain gauge method. The results show that the uniaxial stress distributions of the two steel members can be evaluated by both the Lcr wave time-of-flight (TOF) method and the shear-wave spectrum method, although the latter is more accurate for the measurement of stress distribution. Furthermore, the measurement principles, parametric calibrations, sensitivity, accuracy and repeatability of the two methods are compared, and their applicability is discussed.

This paper proposes a novel vibration-based damage identification method, named the probability distribution of decay-rate (PDDR). By introducing a statistical framework, the PDDR method estimates damage-induced changes in overall damping behaviour of a free-vibration dynamic system. Utilising free-vibration impulse-response (IR) data, a one-dimensional dataset of local maxima-minima points is constructed. A statistical analysis of this dataset is then performed to derive damage-sensitive parameters. It is demonstrated that through the use of a statistical analysis framework, a number of enhancements are attained in terms of both robustness and leniency in estimating the significantly nonlinear property of overall damping. An impact hammer test is conducted in the laboratory to verify the efficacy of the proposed PDDR method. The test was performed on a scale-model steel Warren truss bridge structure, subjected to bolt-connection failures. The comparison results between the PDDR method and the standard experimental modal analysis (EMA) method confirm that the former is effective for damage identification of complex structures, particularly with real experimental data and steel-frame structure assemblies.

This paper presents an experimental study on circular stirrup-confined concrete specimens under uniaxial and monotonic load. The effects of stirrup volume ratio, stirrup yield strength and concrete strength on damage evolution of stirrup-confined concrete were investigated. The experimental results showed that the strength and ductility of concrete are improved by appropriate arrangement of the stirrup confinement. Firstly, the concrete damage evolution can be relatively restrained with the increase of the stirrup volume ratio. Secondly, higher stirrup yield strength usually causes larger confining pressures and slower concrete damage evolution. In contrast, higher concrete strength leads to higher brittleness, which accelerates the concrete damage evolution. A plastic strain expression is obtained through curve fitting, and a damage evolution equation for circular stirrup-confined concrete is proposed by introducing a confinement factor (C) based on the experimental data. The comparison results demonstrate that the proposed damage evolution model can accurately describe the experimental results

Internal stress in structural steel members is an important parameter for steel structures in their design, construction, and service stages. However, it is hard to measure via traditional approaches. Among the existing non-destructive testing (NDT) methods, the ultrasonic method has received the most research attention. Longitudinal critically refracted (Lcr) waves, which propagate parallel to the surface of the material within an effective depth, have shown great potential as an effective stress measurement approach. This paper presents a systematic non-destructive evaluation method to determine the internal stress in in-service structural steel members using Lcr waves. Based on theory of acoustoelasticity, a stress evaluation formula is derived. Factor of stress to acoustic time difference is used to describe the relationship between stress and measurable acoustic results. A testing facility is developed and used to demonstrate the performance of the proposed method. Two steel members are measured by using the proposed method and the traditional strain gauge method for verification. Parametric studies are performed on three steel members and the aluminum plate to investigate the factors that influence the testing results. The results show that the proposed method is effective and accurate for determining stress in in-service structural steel members.

Time-delays of control force calculation, data acquisition and actuator response will degrade the performance of Active Mass Damper (AMD) control systems. To reduce the influence, model-reduction method is used to deal with the original controlled structure. However, during the procedure, the related hierarchy information of small eigenvalues will be directly discorded. As a result, the reduced-order model ignores the information of high-order mode, which will reduce the design accuracy of an AMD control system. In this paper, a new reduced-order controller based on the improved Balanced Truncation (BT) method is designed to reduce the calculation time and to retain the abandoned high-order modal information. It includes high-order natural frequency, damping ratio and vibration modal information of the original structure. Then, a control gain design method based on Guaranteed Cost Control (GCC) algorithm is presented to eliminate the adverse effects of data acquisition and actuator response time-delays in the design process of the reduced-order controller. To verify its effectiveness, the proposed methodology is applied to a numerical example of a ten-storey frame and an experiment of a single span four-storey steel frame. Both numerical and experimental results demonstrate that the reduced-order controller with GCC algorithm has an excellent control effect, meanwhile can compensate time-delays effectively

Severe air pollution and its associated health impacts have become one of the major concerns in China. A detailed analysis of PM2.5 chemical compositions is critical for optimizing pollution control measures. In this study, daily 24-h bulk filter samples were collected and analyzed for totally 21 field campaigns at 17 sites in China between 2008 and 2013. The 17 sites were classified into four groups including six urban sites, seven regional sites, two coastal sites in four fast developing regions of China (i.e. Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta and Sichuan Basin), and two ship cruise measurements covered the East China Sea and Yellow Sea of China. The high average concentrations of PM2.5 and the occurrences of extreme cases at most sites imply the widespread air pollution in China. Fine particles were largely composed of organic matter and secondary inorganic species at most sites. High correlation between the temporal trends of PM2.5 and secondary species of urban and regional sites highlights the uniformly distributed air pollutants within one region. Secondary inorganic species were the dominant contributors to the high PM2.5 concentration in Northern China. However in Southern China, the relative contributions of different chemical species kept constant as PM2.5 increased. This study provides us a better understanding of the current state of air pollution in diversified Chinese cities. Analysis of chemical signatures of PM2.5 could be a strong support for model validation and emission control strategy.

Slab-girder structures composed of steel girder and reinforced concrete slab are widely used in buildings and bridges in the world. Their advantages are largely based on the composite action through the shear connection between slab and girder. In order to assess the integrity of this kind of structures, numerous vibration-based damage identification methods have been proposed. In this study, a scaled composite slab-girder model was constructed in the laboratory. Some removable shear connectors were specially designed and fabricated to connect the girder and slab that were cast separately. Then, a two stage experiment including both static and vibration tests was performed. In the first stage, vibration tests were conducted under different damage scenarios, where a certain number of shear connectors at certain locations were removed step by step. In the second stage, two sets of hydraulic loading equipment were used to apply four point static loads in the test. The loads are increased gradually until concrete slab cracked. The loading histories as well as deflections at different points of the beam are recorded. Vibration test was carried out before and after concrete cracking. Experimental results show that the changes of mode shapes and relative displacement between slab and girder may be two promising parameters for damage identification of slab-girder structures.

The analysis of rock slope stability is a classical problem for geotechnical engineers. However, for practicing engineers, proper software is not usually user friendly, and additional resources capable of providing information useful for decision-making are required. This study developed a convenient tool that can provide a prompt assessment of rock slope stability. A nonlinear input–output mapping of the rock slope system was constructed using a neural network trained by an extreme learning algorithm. The training data was obtained by using finite element upper and lower bound limit analysis methods. The newly developed techniques in this study can either estimate the factor of safety for a rock slope or obtain the implicit parameters through back analyses. Back analysis parameter identification was performed using a terminal steepest descent algorithm based on the finite-time stability theory. This algorithm not only guarantees finite-time error convergence but also achieves exact zero convergence, unlike the conventional steepest descent algorithm in which the training error never reaches zero.

Grouted splice sleeve (GSS) connectors are mainly used in precast concrete structures. However, errors in manual operation during construction cause grouted defects in the GSS connector, which can lead to a negative effect on the overall mechanical properties of the structures. Owing to the complex structure of precast concrete members with a GSS connector, it is difficult to detect grouted defects effectively using traditional ultrasonic parameters. In this paper, a wavelet packet analysis algorithm was developed to effectively detect grouted defects using the ultrasonic method, and a verified experiment was carried out. Laboratory detection was performed on the concrete specimens with a GSS connector before grouting, in which the grouted defects were mimicked with five sizes in five GSS connectors of each specimen group. A simple and convenient ultrasonic detection system was developed, and the specimens were detected. According to the proposed grouted defect index, the results demonstrated that when the grouted defects reached certain sizes, the proposed method could detect the grouted defects effectively. The proposed method is effective and easy to implement at a construction site with simple instruments, and so provides an innovative method for grouted defects detection of precast concrete members.

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet.

Corrosion has significant adverse effects on the durability of reinforced concrete (RC) structures, especially those exposed to a marine environment and subjected to mechanical stress, such as bridges, jetties, piers and wharfs. Previous studies have been carried out to investigate the corrosion behaviour of steel rebar in various concrete structures, however, few studies have focused on the corrosion monitoring of RC structures that are subjected to both mechanical stress and environmental effects. This paper presents an exploratory study on the development of corrosion monitoring and detection techniques for RC structures under the combined effects of external loadings and corrosive media. Four RC beams were tested in 3% NaCl solutions under different levels of point loads. Corrosion processes occurring on steel bars under different loads and under alternative wetting - drying cycle conditions were monitored. Electrochemical and microscopic methods were utilised to measure corrosion potentials of steel bars; to monitor galvanic currents flowing between different steel bars in each beam; and to observe corrosion patterns, respectively. The results indicated that steel corrosion in RC beams was affected by local stress. The point load caused the increase of galvanic currents, corrosion rates and corrosion areas. Pitting corrosion was found to be the main form of corrosion on the surface of the steel bars for most of the beams, probably due to the local concentration of chloride ions. In addition, visual observation of the samples confirmed that the localities of corrosion were related to the locations of steel bars in beams. It was also demonstrated that electrochemical devices are useful for the detection of RC beam corrosion.

Steel-fibre-reinforced concrete (SFRC) has been recognised as an effective solution to resist impact loading on structures. The reliable application and efficient design of SFRC structures depends on the knowledge of its mechanical properties. Since many important factors, including the locations and orientations of fibres and aggregates in concrete and the material properties of concrete matrix, are intrinsically random, the mechanical properties of SFRC present a high level of randomness. To accurately quantify them, effective statistical techniques are indispensable. Using traditional statistical techniques, a large quantity of data, from either experiments or numerical simulations, are needed to derive the correlation between the mechanical properties and the random factors. However, both ways are time-consuming and costly. Therefore, very little information regarding the statistical mechanical properties of SFRC can be found in the current literature. In this study, a kernel-based nonparametric statistical method is proposed to derive the statistical mechanical properties of SFRC with limited number of data. The behaviours of SFRC with randomly distributed spiral-shaped fibres and aggregates under impact loading are simulated using commercial software LS-DYNA. The simulation accuracy is validated by the experimental results. The influences of various volume fractions of fibres on dynamic increase factor (DIF) of the tensile strength of SFRC specimens under dynamic loadings at different strain rates are quantified through a prediction model obtained from kernel regression. The results demonstrate that the proposed method is able to estimate the DIF value of SFRC based on the tensile strength and strain rate, and to derive the statistical mechanical properties of SFRC.

Chirped fiber Bragg grating (CFBG) sensors were embedded within the adhesive bondline of single-lap CFRP-GFRP bonded composite joints. The effect of disbond propagation (as a consequence of fatigue loading) on the reflected spectra from the CFBG sensor has been studied. As the disbond propagates, thermal strains generated during the bonding of the joint at elevated temperature are released and, as a consequence, a peak in the reflected spectra of the CFBG sensor can be seen. Using a transparent GFRP adherend, it has been possible to demonstrate that there is reasonable agreement between the position of the peak in the reflected spectrum and the disbond front position in the bonded joint.

The absolute stress in structural steel members is an important indicator of the performance of steel structures. Among the existing non-destructive testing (NDT) methods, ultrasonic methods have received the most research attention. The existing ultrasonic methods can evaluate the average stress in a fixed acoustic path but cannot easily measure the stress field within the tested objects. We present a non-destructive method to evaluate the absolute stress field in a structural steel member using longitudinal critically refracted (Lcr) waves. Specifically, a theoretical expression is derived for absolute stress measurement. A measurement system is developed to demonstrate the performance of the proposed method. A sensor group, which contains one transmitter and two receiver transducers connected by a Vernier calliper, is designed to transmit and receive Lcr waves. The proposed method is applied to two steel members with variable cross-sections. The traditional strain gauge method is used for verification. The results show that the proposed method can efficiently evaluate the stress distribution and stress extremum in structural steel members.

The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice.

Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data, and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.

To obtain actual conditions of infrastructure assets and manage them more efficiently, extensive research efforts have been placed on structural health monitoring (SHM), especially those using data-driven methods. Recently, deep learning becomes a research hotspot in many application areas, including the SHM domain. Their performance largely relies on the quality and quantity of the training data, obtained either experimentally or numerically. Due to the time and expense restraints, field or laboratory test data are normally limited by the variation of structural conditions, while the quality of numerical simulation data is subjective to experts' modelling skills. Therefore, the actual performance of deep learning algorithms with limited training data needs to be studied, and the alternative ways to generate more training data need to be developed. In this work, we develop a new one-Dimensional Convolutional Neural Network (1D-CNN) for structural condition identification. A laboratory case study is conducted to evaluate the performance of the algorithm. A steel Warren truss bridge structure is constructed and instrumented with accelerometers and impact hammer. The vibration tests under seven different scenarios are conducted, and each scenario has five repeated test data. The algorithm is trained with different quantities of training data (from one test data to four test data for each scenario). The results show that condition identification results become reliable with at least three repeated test data. To overcome the challenge of limited monitoring data, we propose the potential application of Generative Adversarial Networks (GANs) to generate more reliable training data.

Vibration-based condition identification of bolted connections can benefit the effective maintenance and operation of steel structures. Existing studies show that modal parameters are not sensitive to such damage as loss of preload. In contrast, structural responses in the time domain contain all the information regarding a structural system. Therefore, this study aims to exploit time-domain data directly for condition identification of bolted connection. Finite element (FE) model updating is carried out based on the vibration test data of a steel frame, with various combinations of bolts with loss of preload, representing different damage scenarios. It is shown that the match between the numerically simulated and measured acceleration responses of the steel frame cannot be achieved. The reason is that time-dependent nonlinearity is generated in bolted connections during dynamic excitation of the steel frame. To capture the nonlinearity, a virtual viscous damper is proposed. By using the proposed damper alongside the updated system matrices of the FE model, the time domain acceleration responses are estimated with great consistency with the measured responses. The results demonstrate that the proposed virtual damper is not only effective in estimating the time domain acceleration responses in each damage case, but also has the potential for condition identification of bolted connections with such small damage as just one bolt with loss of preload. It can also be applied to other challenging scenarios of condition identification, where modal parameters are not sensitive to the damage.

The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case–control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1–3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families.

As structural systems approach their end of service life, integrity assessment and condition monitoring during late life becomes necessary in order to identify damage due to age-related issues such as corrosion and fatigue and hence prevent failure. In this paper, a novel method of level 3 damage identification (i.e. detection, localisation and quantification) from local vibration mode pair (LVMP) frequencies is introduced. Detection is achieved by observation of LVMP frequencies within any of the vibration modes investigated while the location of the damage is predicted based on the ranking order of the LVMP frequency ratios and the damage is quantified in terms of material volume loss from pre-established quantification relations. The proposed method which is baseline-free (in the sense that it does not require vibration-based assessment or modal data from the undamaged state of the pipe) and solely frequency-dependent was found to be more than 90% accurate in detecting, locating and quantifying damage through a numerical verification study. It was also successfully assessed using experimental modal data obtained from laboratory tests performed on an aluminium pipe with artificially inflicted corrosion-like damage underscoring a novel concept in vibration-based damage identification for pipes.

Waste management is becoming a major issue for communities worldwide. Glass, being nonbiodegradable, is not suitable for addition to landfill, and as such recycling opportunities need to be investigated. Due to the high material consumption of the construction industry, the utilisation of waste glass as a partial replacement for fine aggregate in structural concrete is particularly attractive. This project aimed to determine the level of glass replacement resulting in optimal compressive strength. Three concrete samples were tested at 7 and 28 days, for glass replacement proportions of 15, 20, 25, 30 and 40%. Compressive strength was found to increase up to a level of 30%, at which point the strength developed was 9% and 6% higher than the control after 7 and 28 days respectively. This demonstrates that concrete containing up to 30% fine glass aggregate exhibits higher compressive strength development than traditional concrete

Among different Structural Health Monitoring (SHM) systems applied on bridges, Bridge Weight-in-Motion (BWIM) is probably the one with widest applications worldwide. Briefly, BWIM uses on-structure sensors that are able to acquire signals sensitive to traffic load events, which can be used as an indirect indicator of the load magnitude. The sampling rate required for this is relatively high (at least 10 Hz), which usually lead to databases with sizes that might reach the order of gigabytes. It is impractical to process this volume of information in the context of infrastructure asset management. Hence, an effective and efficient method for the compression and storage of BWIM data is becoming mandatory. In this paper, sparse representation algorithms have been innovatively applied to the BWIM data compression. A comparative study is performed based on measurements collected from a real bridge, by exploring different methods including Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and two dictionary learning methods, i.e. Compressive Sensing (CS) and K-means Singular Value Decomposition (K-SVD). It has been found that the K-SVD method shows the best performance when applied to this specific type of data, while the DWT method using Haar wavelet is the most computationally efficient. Nearly lossless reconstruction of the signal is achieved by using K-SVD with less than 0.1 % reserved coefficients, which gives evidence that dictionary learning technologies are feasible to guarantee the same level of information even with much smaller databases. Therefore, the utilization of dictionary learning is a clear step forward towards higher levels of efficiency in the compression and storage of data collected by SHM systems.

Monitoring of stress/strain is of particular importance to understand better the mechanical pa-rameters which underpin the condition of a wide variety of structures. Although they were initially developed some 30 years ago, optical fibre sensors have not been widely used in this field and the traditional strain gauges still dominate the market. During recent years, optical fibre sensors, especially Fibre Brag Grating (FBG) sen-sors, have received intensive attention with considerable research and development to allow them to be applied successfully in many engineering fields, including in a number of structural health monitoring systems. How-ever, ensuring the structural integrity of the FBG sensor and providing robust ‘packaging’ for the device remains a key issue, which becomes a major concern when applying these in various structural strain test methods. To protect FBG sensors in use, a number of packaging techniques have been proposed, including metal, fibre re-inforcement polymers and different types of epoxy. However, often such packaging is either insufficiently flexible or expensive. The recent development of 3D printing technologies provides a new and rapid solution for the packaging of FBG sensors, which allows the user easily to define the size and property of the sensor package tailoring that to the specific application. In this study, to examine the use of these techniques for struc-tural monitoring, several different printing materials were used with FBG-based sensors and their properties compared, with the optimal sizes for the packages used being identified. Experimental studies were conducted on a steel beam under repeated stepped loading and unloading processes with different sensor packages. To compare the performances of the traditional strain gauge and these FBG-based sensors, both types were attached on the beam to record the strain changes during the tests. The results demonstrate that the 3D printed packaged sensor designs are highly suitable for use in structural strain testing, and that the performance of FBG sensor thus configured is as stable and consistent in performance as is the familiar strain gauge counterpart.

Large-span steel frame structures prove to be an ideal choice for their speed of construction, relatively low cost, strength, durability and structural design flexibility. For this type of structure, the beam-column connections are critical for its structural integrity and overall stability. This is because a steel frame generally fails first at its connectors, due to the change in stress redistribution with adjacent members and material related failures, caused by various factors such as fire, seismic activity or material deterioration. Since particular attention is required at a steel frame’s connection points, this study explores the applicability of a comprehensive structural health monitoring (SHM) method to identify early damage and prolong the lifespan of connection points of steel frames. An impact hammer test was performed on a scale-model steel frame structure, recording its dynamic response to the hammer strike via an accelerometer. The testing procedure included an intact scenario and two damage scenarios by unfastening four bolt connections in an accumulating order. Based entirely on time-domain experimental data for its calibration, an Auto Regressive Average Exogenous (ARMAX) model is used to create a simple and accurate model for vibration simulation. The calibrated ARMAX model is then used to identify various bolt-connection related damage scenarios via R2 value. The findings in this study suggest that the proposed time-domain approach is capable of identifying structural damage in a parsimonious manner and can be used as a quick or initial solution.

The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice.

Infrastructure performance is of great importance for a nation’s economy and its people’s quality of life. For efficient and effective infrastructure asset management, structural health monitoring (SHM) has been researched extensively in the past 20-30 years. With an increasing number of SHM systems being installed, the interpretation of the large volume of monitoring data, i.e. often manifested as condition identification, becomes essential in asset integrity management. This paper provides an appraisal of existing literature reviews on SHM, considering both reviews on different types of structures and those focused on different approaches for data interpretation. It explores the evolution of research interests in this field and identifies the need for an integrated physics-based and data-driven structural condition identification approach.

Since failures in sensors will degrade the performance of Active Mass Damper (AMD) control systems, a dynamic filter design method, a state observer design method, and a robust control strategy are developed and presented in this paper to overcome this deficiency. The filter design method will be transformed into a H2/H∞ control problem that can be solved by Linear Matrix Inequality (LMI) approach. Thus, it can be used to perform fault detection and isolation (FDI) for the control systems. And, the state observer design method uses the acceleration responses as the feedback signal. The detected and isolated fault signals in accelerometers are used to estimate the whole states that are used to calculate the control force though a robust control strategy based on regional pole-assignment algorithm. Then, the active fault-tolerant control (FTC) will be accomplished. To verify its effectiveness, the proposed methodology is applied to a numerical example of a ten-storey frame and an experiment of a single span four-storey steel frame. Both numerical and experimental results demonstrate that the performances of FTC controller and the control system will be improved by the designed dynamic FDI filter and that it can effectively detect and isolate fault signal.

An increasing number of tubular steel structures have exceeded their design service lives; hence, monitoring of these structures is crucial in preventing any unforeseen failures and corresponding catastrophic consequences - safety or economic. As is well known, vibration-based structural health monitoring (SHM) presents non-destructive methods for damage identification, though their application in corrosion problems appears somewhat limited. Furthermore, majority of the SHM techniques reported in literature deal with prismatic or beam-like members; tubular structures have received less research attention. In this paper, numerical models of a pipe in its intact and corroded conditions are built and analysed using ABAQUS®. Modal parameters extracted from analyses results are utilised to detect, locate and quantify corrosion. A potential indicator “Normalised Displacement Modeshape (NDM)” is introduced and tested alongside existing damage indicators. Results from the employed indicators are compared and the capabilities of each indicator in identification of the investigated corrosion patterns are discussed.

Finite Element (FE) model updating has been attracting research attentions in structural engineering fields for over 20 years. Its immense importance to the design, construction and maintenance of civil and mechanical structures has been highly recognised. However, many sources of uncertainties may affect the updating results. These uncertainties may be caused by FE modelling errors, measurement noises, signal processing techniques, and so on. Therefore, research efforts on model updating have been focusing on tackling with uncertainties for a long time. Recently, a new type of evolutionary algorithms has been developed to address uncertainty problems, known as Estimation of Distribution Algorithms (EDAs). EDAs are evolutionary algorithms based on estimation and sampling from probabilistic models and able to overcome some of the drawbacks exhibited by traditional genetic algorithms (GAs). In this paper, a numerical steel simple beam is constructed in commercial software ANSYS. The various damage scenarios are simulated and EDAs are employed to identify damages via FE model updating process. The results show that the performances of EDAs for model updating are efficient and reliable.

Structures inevitably deteriorate during their service lives. Therefore, the methods capable of identifying and assessing various damages in a structure timely and accurately have drawn increasing attention. From a broader perspective, structural damage identification problem can be regarded as a pattern recognition problem by using sparse representation techniques. The unknown signal/feature from a damaged structure can be associated to a known type of signal/feature in a “dictionary”, leading to damage identification. From this new angle, an innovative damage identification scheme has been proposed by the authors. In this paper, two important techniques of this scheme are further discussed, namely the construction of dictionary and the choice of parameters. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme will be a promising tool for structural health monitoring.

The impact-hammer test is a widely known non-destructive vibration testing method to extract the modal parameters of a dynamic system. Due to the fast development of computing, timedomain analysis for data interpretation is now receiving increased attention. To provide normalised and denoised data, appropriate signal processing is indispensable. This paper presents a practical study on data normalisation and denoising. Specifically, discrete wavelet transform (DWT) denoising filter and weighted moving average (WMA) filter are applied in sequence for denoising. The signal processing results on the impact hammer test of a steel frame are shown to demonstrate the effectiveness of the proposed methodology.

Non-destructive measurement of stress can provide an effective way to explore the service life and performance degradation status of steel structures. In this paper, a measurement system is designed and developed, which includes both hardware and software systems. The hardware system consists of three modules: signal transmitting, signal conversion and signal receiving. The software system consists of four modules: signal storage, signal de-noising, calibration of stress to acoustic time difference factor, and stress calculation. To examine the performance of the system, a group of axial forces are applied on two steel members and axial stresses are measured on designed system. The strain gauge method is used for verification. The results show that the designed system is reliable and agrees with the results from strain gauge method. It has high potential to be applied in the field stress evaluation to monitor the structure, from pre-operation stage to service operation stage.

Guided-wave-based structural damage identification techniques have received more and more attention in the civil engineering community. They not only have the capability of detecting smaller damages on a structure than vibration-based methods, but also can cover a relatively larger magnitude, compared with other traditional non-destructive evaluation techniques. To realize damage identification, features usually need to be extracted from the time domain responses. This is achievable for homogeneous materials, including steel and aluminum. But for composite materials, such as concrete, the features are usually very difficult to be extracted, because of the existence of small aggregates and the nature of uneven material properties which generate multiple reflections. It is very difficult to simulate the time domain responses and to identify damages by using time domain responses directly for such random material. Recently, a new damage identification scheme is proposed, named as DISC (Damage Identification based on Sparse Coding). This method is essentially a pattern recognition technique, which avoids the traditional fixed transform process but takes advantage of the existing data by dictionary learning techniques. This paper will review the DISC method and then apply it to identification of de-bonding damage in concrete beam using guided wave test data. The results will demonstrate the effectiveness of the DISC methodology.

Structures inevitably deteriorate during their service lives. To accurately evaluate their structural condition, the methods capable of identifying and assessing damage in a structure timely and accurately have drawn increasing attention. Compared to widely-used frequency-domain methods, the processing of time-domain data is more efficient, but remains difficult since it is usually hard to discern signals from different conditions. In fact, the signal processing fields have observed the evolution of techniques, from such traditional fixed transforms as Fourier, to dictionary learning (DL). DL leads to better representation and hence can provide improved results in many practical applications. In this paper, an innovative time-domain damage identification algorithm is proposed from a DL perspective, using D-KSVD algorithm. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme is a promising tool for structural health monitoring.

It has been well demonstrated that the impact loading resistance capacity of the concrete material can be effectively increased by adding fibres. Recent studies proved that compared to other conventional steel fibres, using steel fibres with spiral shape further increases the post-failure energy absorption and crack stopping capacities of concrete because of the better bonds in the concrete matrix and larger deformation ability. The present study conducts high rate impact tests using split Hopkinson pressure bar (SHPB) to further investigate the dynamic compressive properties of spiral fibre reinforced concrete (SFRC). SFRC specimens with different volume fractions of fibres ranging from zero to 1.5% are prepared and tested. The influences of different volume fractions of fibres on strength, stress-strain relation and energy absorption of SFRC specimens under quasi-static and dynamic loadings are studied. In SHPB compression tests, the strain rate achieved ranges from 50 1/s to 200 1/s. Highspeed camera is used to capture the failure processes and failure modes of SFRC specimens with different fibre volume fractions during the tests for comparison. Dynamic stress-strain curves under different strain rates are derived. The energy absorption capacities of the tested specimens are obtained and compared. Strain rate effects on the compressive strength are also discussed. The corresponding empirical DIF (dynamic increase factor) relations for SFRC are proposed.

This paper introduces a novel vibration-based damage identification method, named the probability distribution of decay-rate (PDDR). The PDDR method is a time-domain statistical damping estimate for damage detection and identification applications. Suitable for both SDOF and MDOF dynamic systems, a mode-superposed damping estimation is determined based on time-domain based observations of an impulse response (IR) decay rate. Firstly, a detailed literature survey and discussion are presented to show the limitations of existing methods. Secondly, the overall process for PDDR implementation is succinctly introduced. Lastly, a hypothetical MDOF model was formulated and used to demonstrate the first phase implementation of the PDDR method. The results show the proposed method is sensitive to the change of damping parameters, which is usually more sensitive to structural damage, and therefore can be used complimentarily with traditional vibration-based methods.

The reliable and efficient design of steel-fibre-reinforced concrete (SFRC) structures requires clear knowledge of material properties. Since the locations and orientations of aggregates and fibres in concrete are intrinsically random, testing results from different specimens vary, and it needs hundreds or even thousands of specimens and tests to derive the unbiased statistical distributions of material properties by using traditional statistical techniques. Therefore, few statistical studies on the SFRC material properties can be found in literature. In this study, high-rate impact test results on SFRC using split Hopkinson pressure bar are further analysed. The influences of different strain rates and various volume fractions of fibres on compressive strength of SFRC specimens under dynamic loadings will be quantified, by using kernel regression, a kernel-based nonparametric statistical method. Several kernel estimators and functions will be compared. This technique allows one to derive an unbiased statistical estimation from limited testing data. Therefore it is especially useful when the testing data is limited.