Dr Donya Hajializadeh

Meeting the 2050 net zero carbon target requires all sectors of civil engineering to include the reduction of carbon emissions within their designs. This paper presents the development and early achievements of a Carbon Reduction Design Tool (CReDiT) for excavation and clean cover remediation of brownfield sites. The tool was developed for this remediation technique as it is the dominant remediation method used on residential redevelopment sites within the UK. CReDiT determines carbon emissions from the complex processes involved in this form of remediation, e.g., excavation of soil, use of raw materials, transport of material and the waste, recycling and reuse of materials. The application of the tool, to evaluate carbon emissions from excavation and cover remediation options applied to a former landfill site, is presented. In this case study, CReDiT quantifies carbon emissions and material volumes for proposed design options. It also demonstrates the carbon savings that can be made by the effective reuse of material onsite and the contribution of waste materials to carbon emissions. Additional carbon savings through the reuse or recycling of carbon-rich or valuable materials are also calculated. The use of CReDiT has led to a rethink of remediation excavation and cover design. Excavated /waste materials are re-evaluated as a resource; material reuse options are assessed and carbon reduction is included in the design process. This leads to more sustainable remediation solutions.

Recent years have seen a paradigm shift in assessing the performance of assets in response to disruptive hazards, in that resilience is seen as a more inclusive and over-arching decision variable. This shift in decision drivers provides a better picture of asset behavior in response to hazardous events such as earthquakes and hurricanes. Since highway bridges are among the most critical and vulnerable components of transportation networks, evaluating their functionality under extreme events leads to well-informed decision-making. Whilst there is an ever-growing interest in resilience-based hazard assessment in a wide range of infrastructure sectors, there is limited attention on identifying resilience drivers as a function of hazard and asset characteristics. To this end, this paper presents a framework for probabilistic resilience assessment of a cohort of common highway bridges subjected to a wide range of ground acceleration intensities. This study presents the first ensemble learning-based predictive model using bagging and boosting techniques to predict resilience index as a function of seismic events and asset characteristics on bridge resilience. The hypermeters and input structure of the predictive model are optimized to reduce complexity and maximize efficiency. The findings show that the proposed model performs with a 75–95% success rate in predicting resilience as a function of structural characteristics and peak ground acceleration. This model provides useful insights on the impact of various parameters and drivers of resilience in concrete box-girder bridges.

Optimizing the serviceability of highway bridges is a fundamental prerequisite to provide proper infrastructure safety and emergency responses after natural hazards such as an earthquake. In this regard, fragility and resilience assessment have emerged as important means of describing the potential seismic risk and recovery process under uncertain inputs. Generating such assessments requires estimating the seismic demand of bridge components consisting of piers, deck, abutment, bearing, etc. The conventional probabilistic model to estimate the seismic demands was introduced more than two decades ago. Despite an extensive body of research ever attempting to improve demand models, the univariate demand model is the most common method used in practice. This work presents a comprehensive review of the evolution of demand models capturing machine-learning-based methodologies and their advantage in comparison to the conventional model. This study sheds light on understanding the existing demand models and their associated attributes along with their limitations. This study also provides an appraisal of the application of probabilistic demand models to generate fragility curves and subsequent application in the resilience assessment of bridges. Moreover, as a sound reference, this study highlights opportunities for future development leading to enhancement of the performance and applicability of the demand models.

The common challenge currently faced by critical infrastructure (CI) asset owners and operators is the lack of an integrated and robust resilience-informed business planning and management approach in response to interdependent assets’ failures, in particular due to low-probability/high-impact environmental hazards. Interdependencies among CI can cause cascading failures and hence, amplify impacts due to these failures. This can also affect CI’s service restoration rate and consequently, reducing their resilience in coping with these hazardous events. As infrastructures are becoming more interdependent in some sectors, there is an increasing need for better management of the interactions and interdependencies. To reduce these impacts, an integrated resilience and vulnerability- informed Decision Support System (DSS) is required to identify interdependent network’s vulnerable components and introduce adaptive capacities accordingly. This is of particular importance given ever growing investments in asset management across different sectors in order to improve the resilience of the networks in response to extreme environmental hazards. This study presents a novel framework for building a resilience and vulnerability-informed decision support system (RV-DSS). This framework provides potential means of communicating challenges induced due to interdependencies and quantifies benefits of considering interdependencies in streamlining intervention strategies for systems. It also proposes a measure of network resilience in response to hazardous events, in addition to the commonly used measures of vulnerability for assessment of the network performance. The framework can be used in initiating the interdependency-based communications among different CI network owners and managers, leading to shared knowledge and common understanding of their connected assets, hidden failure propagation mechanisms and collective recovery process. The application of the framework is then demonstrated using a case study in North Argyll, Scotland. It is quantitatively demonstrated that although infrastructures with a higher level of interdependency, can impose the network to higher vulnerability, it provides a greater opportunity for an integrated recovery

Critical infrastructures (CIs), provide essential services to the society. As infrastructures are becoming more interdependent, there is an increasing need for better management of their interactions and interdependencies. Interdependencies among CI can cause cascading failures and, hence, amplify negative consequences due to these failures. This can also affect CIs' service restoration rate and consequently reduce their resilience in coping with these hazardous events. The common challenge currently faced by CI asset owners is the lack of robust resilience-informed business planning and management strategies in response to interdependent assets' failures due to low-probability/high-impact hazards. This is of particular importance as CI owners and managers are investing more on improving the resilience of their assets in response to extreme environmental hazards. This study has approached CIs nexus from the interdependency management point of view. It has developed an integrated resilience assessment framework to identify and map interdependency-induced vulnerabilities in critical infrastructure networks. This framework can potentially support effective management of the interdependencies in CI networks. The findings have been reflected in mapping the connection between the changes in resilience due to interdependency-induced failures and the cost of intervention scenarios, providing means of exploring shared intervention strategies.

This study presents a novel machine-learning-based approach for damage detection using train-borne measurements, under operational conditions (speed > 50 km/h, rail irregularities and noise). To this end, an optimised two-dimensional convolutional neural network with network-in-network architecture is built, trained and tested to detect damage of various severity levels and locations in a bridge, using train-borne measurements only. As an input, cross-correlation of signals from two train bogies is used as a damage-sensitive feature for the first time. The proposed method in this study is applied to a cohort of simulated acceleration measurements on a nominal RC4 power car passing over a 25 m simply supported reinforced concrete bridge. The presented method shows great accuracy in detecting damage under operational conditions. The sensitivity and robustness of the approach are tested and validated for 18 damage severity and location scenarios and 100 random vehicle speeds, ranging between 70 and 130 km/h. This is of particular value, as speed defines the length of the train-borne signal while passing over the bridge and hence the amount of information manifested in a single passing. The results demonstrate the feasibility of the approach for data-driven damage detection using measurement on an instrumented passing train.

Critical infrastructures (CIs), provide essential services to the society. As infrastructures are becoming more interdependent, there is an increasing need for better management of their interactions and interdependencies. Interdependencies among CI can cause cascading failures and, hence, amplify negative consequences due to these failures. This can also affect CIs' service restoration rate and consequently reduce their resilience in coping with these hazardous events. The common challenge currently faced by CI asset owners is the lack of robust resilience-informed business planning and management strategies in response to interdependent assets' failures due to low-probability/high-impact hazards. This is of particular importance as CI owners and managers are investing more on improving the resilience of their assets in response to extreme environmental hazards. This study has approached CIs nexus from the interdependency management point of view. It has developed an integrated resilience assessment framework to identify and map interdependency-induced vulnerabilities in critical infrastructure networks. This framework can potentially support effective management of the interdependencies in CI networks. The findings have been reflected in mapping the connection between the changes in resilience due to interdependency-induced failures and the cost of intervention scenarios, providing means of exploring shared intervention strategies.

Random Field theory has emerged in recent years to model the statistical correlation of resistance in concrete structures and to determine its influence on the probability of structural failure. A major shortcoming in the work carried out to date is the spatial variability and corresponding correlation associated with applied traffic loads. In this paper the influence of spatial correlation of both traffic load and resistance is considered in the context of bridge safety assessment. The current study, explores, the nature of the problem by three theoretical examples. As a general trend, examples show that while traffic loads are weakly correlated, load effects are strongly correlated as the same heavy vehicle often causes extremes of load effect in different parts of the bridge which is due to the transverse sharing of load (measured here using a load sharing factor). It is found that the strength of correlation of load effect depends greatly on the load sharing factor which is treated in a simple way in many studies. In a more sophisticated beam-and-slab bridge example, load sharing factors are derived from a finite element analysis to assess transverse load sharing, and are shown to vary by girder number, girder segment and by load location. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. © 2015 Elsevier B.V.

In this study a structural health monitoring (SHM) system is combined with bridge weigh-in-motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the ‘virtual monitoring’ concept, where all parts of the bridge that are not monitored directly using sensors, are ‘virtually’ monitored using the load information and a calibrated finite element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed virtual monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. To test the accuracy of the virtual monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing. © 2017, Canadian Science Publishing. All rights reserved.

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, 200 randomly generated 2-axle trucks pass over a 6 mlong simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, 77 pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM. © 2018, Canadian Science Publishing. All rights reserved.

Resistance and loads are often correlated in time and space. The paper assesses the influence of these correlations on structural reliability/probability of failure for a typical two-lane reinforced concrete (RC) slab bridge under realistic traffic loading. Spatial variables for structural resistance are cover and concrete compressive strength, which in turn affect the strength and chloride-induced corrosion of RC elements. Random variables include pit depth and model error. Correlation of weights between trucks in adjacent lanes and inter-vehicle gaps are also included and are calibrated against weigh-in-motion data. Reliability analysis of deteriorating bridges needs to incorporate uncertainties associated with parameters governing the deterioration process and loading. One of the major unanswered questions in the work carried out to date is the influence of spatial variability of load and resistance on failure probability. Spatial variability research carried out to date has been mainly focused on predicting the remaining lifetime of a corroding structure and spatial variability of material, dimensional and environmental properties. A major shortcoming in the work carried out to date is the lack of an allowance for the spatial variability of applied traffic loads. In this article, a two-dimensional (2D) random field is developed where load effects and time-dependent structural resistance are calculated for each segment in the field. The 2D spatial time-dependent reliability analysis of an RC slab bridge found that a spatially correlated resistance results in only a small increase in probability of failure. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. © 2015 Informa UK Limited, trading as Taylor & Francis Group.

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined. © 2018 IEEE.

Bridge weigh-in-motion (B-WIM) is a method by which the axle weights of a vehicle travelling at full highway speed can be determined using a bridge instrumented with sensors. This paper looks at the history of B-WIM, beginning with early work on weigh-in-motion (WIM) technologies in the 1960s leading to its invention by Fred Moses and George Goble in the USA in the mid 1970s. Research initiatives in Australia and Europe have focused on improving B-WIM accuracy. The moving force identification (MFI) method models the dynamic fluctuation of axle forces on the bridge and holds particular promise. B-WIM accuracy depends on bridge site conditions as well as the particular data processing algorithm. The accuracy classifications of several B-WIM installations reported in the literature are summarised in this paper. Current accuracy levels are sufficient for selecting vehicles to be weighed using static scales, but insufficient for direct enforcement. Copyright © 2014 Inderscience Enterprises Ltd.

This study describes the development and testing of a railway bridge weigh-in-motion (RB-WIM) system. The traditional bridge WIM (B-WIM) system developed for road bridges was extended here to calculate the weights of railway carriages. The system was tested using the measured response from a test bridge in Poland, and the accuracy of the system was assessed using statically-weighed trains. To accommodate variable velocity of the trains, the standard B-WIM algorithm, which assumes a constant velocity during the passage of a vehicle, was adjusted and the algorithm revised accordingly. The results showed that the vast majority of the calculated carriage weights fell within ±5% of their true, statically-weighed values. The sensitivity of the method to the calibration methods was then assessed using regression models, trained by different combinations of calibration trains.

Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, long run simulations, whereby thousands of years of traffic are simulated, have allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the long-run simulation are applied to a non-linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and one-dimensional (1D) linear and nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple 1D (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e., 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear and nonlinear analyses. ©, 2015, National Research Council of Canada. All Rights Reserved.

HL-93, the current bridge traffic load model used in the United States is examined here. Weigh-in-motion (WIM) data from 17 sites in 16 states containing 74 million truck records are used to assess the level of consistency in the characteristic load effects (LEs) implied by the HL-93 model. The LEs of positive and negative bending moments and shear force are considered on single- and two-lane same-direction slab and girder bridges with a range of spans. It is found that the ratio of WIM-implied LE to HL-93 LE varies considerably from one LE to another. An alternative model is proposed that achieves improvements in consistency in this ratio for the LEs examined, especially for the single-lane case. The proposed model consists of a uniformly distributed load whose intensity varies with bridge length. © 2014 American Society of Civil Engineers.

This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalised Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process. © 2015 Elsevier Ltd.

A framework is presented for the assessment of the safety of a bridge deck under actual traffic loading using an enhanced Monte Carlo method which attempts to reduce computational cost while preserving the advantages of more conventional, computationally intensive, simulation. To generate the bridge loading scenarios, an extensiveWeigh-in-Motion (WIM) database is used to calibrate a sophisticated simulation model of two-directional traffic. Traffic and vehicle characteristics are generated from statistical distributions derived from measured traffic data. Two examples are used in this study to assess the usefulness and accuracy of the enhanced method. In the first, a simple example is used for which the exact theoretical probability of failure is available. Hence, the error in estimation can be assessed directly. In the second, 'long-run' simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. © 2013 Taylor & Francis Group, London.

Railways 2016: The Third International Conference on Railway Technology: Research, Development and Maintenance, Sardinia, Italy, 5-8 April 2016 In a traditional deterministic assessment, a dynamic amplification factor (DAF) is applied to the static loading in order to account for dynamics. The codified DAF values are appropriately conservative in order to consider the wide range of structures and load effects to which they are applied. In the current analysis, a site specific assessment dynamic ratio (ADR) is calculated from direct measurement on an 80 year old steel truss Railway Bridge. The ADR is defined as the ratio of characteristic total stress to the characteristic static stress. The application of ADR is a relatively new concept which has rarely been considered for railway bridges. An assessment performed on the bridge in question showed a decrease in the dynamic allowance when considering the site specific ADR, corresponding to a 26% decrease in calculated stress. The measurements available were also used to derive a robust stochastic model for dynamic allowance which considered the correlation between DAF and stress level. The developed model was applied to a probabilistic assessment and resulted in a 9% increase in reliability.

12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), Vancouver, Canada, 12 -15 July, 2015 The recent extreme weather events in Europe and around the world have raised issues about the organization and management of critical infrastructure. There is uncertainty and a lack of information on how infrastructure should be managed when subject to these extreme events. The existence of chaos and uncertainty in these situations can result in disruptions to transport, power outages and in the most extreme instances, loss of life. The 7th Framework RAIN (Risk Analysis of Infrastructure Networks in response to extreme weather) project is addressing these issues, involving partners from Ireland, Belgium, Germany, Finland, Italy, Netherlands, Slovenia and Spain. The objective of the RAIN project is to provide an operational analysis framework to minimize the impact of major weather events in the EU. This paper summarizes the work that will be performed in one of the work packages of the RAIN project. This work package will examine the impact of critical infrastructure failure on society, security issues and the economy. Based on a risk analysis framework, a means of quantifying the level of risk will be established, firstly due to single land transport mode failures, and secondly for selected multi-mode-interdependent failure scenarios (e.g., failure of power stations result in failure of electrical train lines). In this study, methods will be developed to create an advanced risk assessment procedure, using a probabilistic based approach, to derive a measurable indicator of risk. The risk procedure will be benchmarked against case studies conducted on critical transport and operational tactical connections. The project outputs will contribute to the process of knowledge management used in the protection of Critical Infrastructure and will provide a basis for the development of decision support systems. European Commission - Seventh Framework Programme (FP7)

This paper proposes a novel bridge damage identification methodology using simulated rotation measurements. To illustrate the concept, a numerical 1D beam model subject to a one-axle moving vehicle model is presented. Different bridge segmentation strategies, damage scenarios, and measurement points are considered to demonstrate the detection, localization, and severity quantification capability of the proposed approach. To extend the application to a bridge subject to a fleet of unweighed multiaxle vehicles, the proposed method is combined with an iterative Bridge Weigh-in-Motion algorithm to obtain vehicle weights, bridge damage presence, and location. The results indicate that even if the vehicle weights and damage location are unknown, the proposed method can successfully detect and localize damage. The severity of the damage can also be predicted with reasonable accuracy. Finally, the proposed damage identification system is applied to measurements simulated with a fully calibrated (using in situ measurements) 3D finite-element model of a simply supported multi-T-girder bridge. Results confirm that the proposed system can accurately and automatically detect, localize, and quantify the severity of the defect for a range of cases.

There is considerable uncertainty about what level of traffic loading bridges should be designed for. Codes specify notional load models, generally to represent extreme levels of normal traffic, but these are often crude and have inconsistent levels of safety for different load effects. Over the past few decades, increasing quantities of reliable truck weight data has become available and it is now possible to calculate appropriate levels of bridge traffic loading, both for specific bridges and for a road network. Bridge Traffic Loading brings together experts from all over the world to deliver not just the state-of-the-art of vertical loading, but also to provide recommendations of best-practice for all the major challenges in the field - short-span, single and multi-lane bridge loading, dynamic allowance and long-span bridges. It reviews issues that continue to be debated, such as which statistical distribution is most appropriate, whether free-flowing or congested traffic governs and dealing with future traffic growth. Specialist consultants and bridge owners should find this invaluable, as will regulators.

Through the regulatory planning process, UK local government has detailed information on contaminants for most of their development (predominantly brownfield) sites, dating back to 2000. The soil data collected includes information on polycyclic aromatic hydrocarbons, PAH, a group of potentially carcinogenic chemicals. This paper describes how an evidence-based portfolio of PAH contamination has been generated from this site measured dataset and developed into a process route map to facilitate PAH data analyses in other regions. The PAH data, site history and borehole information were extracted from 1990 to 2019 records of 120 brownfield sites in Elmbridge Borough, a suburban region southwest of London. The data were interrogated using spatial and depth analysis, diagnostic ratios and chemical composition analysis. Elevated PAH concentrations were predominantly found within Made Ground and indicated that the major source was anthropogenic deposition. The results from the diagnostic ratios indicated a pyrogenic, non-traffic related source for 90% of the samples. Statistical analysis showed low median concentration values with a few high outliers for all PAHs studied. Most soil samples had a similar PAH composition, irrespective of site history or soil sample description. This PAH composition was found on residential sites, where the likely source was coal-based burning. The data thus suggested a coal-based source for most of the PAH contamination in the study area. The results for PAH contamintation on brownfield sites in suburban Surrey were similar to a UK study for a UK coal-based industrial area. The PAH concentrations found were significantly higher than those reported in other international studies, however this was partly due to different sampling techniques. A process route map is proposed which incorporates both the analytical processes and the evidence-based portfolio of key findings. This route map is portable to different regions and consequently can be used as a platform for the analysis of PAH data to inform developers and regulators of potential sources and distribution in a region. [Display omitted] •Site evidence of UK suburban brownfield PAH concentrations and distribution presented.•Diagnostic ratios indicated pyrogenic, non traffic source.•Anthropological, but non airborne, deposition of PAH contamination determined.•PAH composition was similar on 120 sites, reflecting a probable coal-based source.•Route map of PAH analytical processes proposed for application to any area.

With the ever-increasing number of well-aged bridges carrying traffic loads beyond their intended design capacity, there is an urgency to find reliable and efficient means of monitoring structural safety and integrity. Among different attempts, vibration-based indirect damage identification systems have shown great promise in providing real-time information on the state of bridge damage. The fundamental principle in an indirect vibration-based damage identification system is to extract bridge damage signatures from on-board measurements, which also embody vibration signatures from the vehicle and road/rail profile and can be contaminated due to varying environmental and operational conditions. This study presents a numerical feasibility study of a novel data-driven damage detection system using train-borne signals while passing over a bridge with the speed of traffic. For this purpose, a deep Convolutional Neural Network is optimised, trained and tested to detect damage using a simulated acceleration response on a nominal RC4 power car passing over a 15 m simply supported reinforced concrete railway bridge. A 2D train–track interaction model is used to simulate train-borne acceleration signals. Bayesian Optimisation is used to optimise the architecture of the deep learning algorithm. The damage detection algorithm was tested on 18 damage scenarios (different severity levels and locations) and has shown great accuracy in detecting damage under varying speeds, rail irregularities and noise, hence provides promise in transforming the future of railway bridge damage identification systems.

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.

The aim of this paper is to propose a methodological framework for consequence analysis of transportation networks. The probabilistic framework is based on the definition of performance indicators that describe the time-dependent functionality of the asset/system, starting from a pre-existing normal performance state, capturing the time and evolution of disruption during and after the disruption and during the recovery/restoration stage. A proposed case study that will be used for the demonstration of the applicability of the framework is described.

Infrastructure networks do not exist in isolation. Rather they are interconnected to other infrastructures and, as technological development increases, so too does the linkage between networks. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. For example, failure of the water drain and sewer system due to 2002 Glasgow flooding affected many homes and closed many main roads and stations such as the A82 and A8 roads, Buchanan Street subway station and Dalmarnock through to Exhibition Centre stations on the Argyle Line. As infrastructures are becoming more interdependent at some sectors, there is an increasing demand for more effective management of these interactions and interdependencies. This paper provides details of a quantitative metric for the robustness, recoverability, rapidity and resourcefulness of the interdependent infrastructure network in response to hazardous event. By generating a quantitative measure of network resilience, considering infrastructure interdependencies, the most severe failure scenarios and their spatial impacts can be identified and mapped. This can lead to prioritise future business planning strategies for CI asset owners and managers. To illustrate the application of the proposed approach, a case study in North Argyll, Scotland is analysed and presented in this paper.

Promoting the resilience of critical infrastructure, when subjected to different hazardous events, is vital. However, applying inappropriate and/or imprecise resilience metrics or quantification techniques could increase the costs of resilience enhancement and reduce its effectiveness in critical infrastructure. This paper develops a method to evaluate and compare different resilience quantification techniques, in relation to different system failure states, in order to measure their effectiveness

Bridge structures are key components of highway infrastructure and their safety is clearly of great importance. Safety assessment of highway bridges requires accurate prediction of the extreme load effects, taking account of spatial variability through the bridge width and length. This concept of spatial variability i s also known as random field analysis. Reliability - based bridge assessment permits the inclusion of uncertainty in all parameters and models associated with the deterioration process. Random field analysis takes account of the probability that two points n ear each other on a bridge will have correlated properties. This method incorporates spatial variability which results in a more accurate reliability assessm ent. This paper presents an integrated model for spatial reliability analysis of reinforced concre te bridges that considers both the bridge capacity and traffic load. A sophisticated simulation model of two - directional traffic is used to determine accurate annual maximum distributions of load effect. To generate the bridge loading scenarios, an extensive Weigh-in-Motion (WIM) database, from five European countries, is used. For this, statistical distributions for vehicle weights, inter - vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for a Monte Carlo simulation of traffic. Results are presented for bidirectional traffic, with one lane in each direction, with a total flow of approximately 2000 trucks per day.

This paper considers the problem of assessing traffic loading on road bridges. A database of European WIM data is used to determine accurate annual maximum distributions of load effect. These in turn are used to find the probability of failure for a number of load effects. Using the probability of failure as the benchmark, traditional measures of safety - factor of safety and reliability index - are reviewed. Both are found to give inconsistent results, i.e., a given factor of safety or reliability index actually corresponds to a range of different probabilities of failure. © 2012 Taylor & Francis Group.

With the increasing cost of energy the need to provide energy efficient buildings continues to grow. In 2003 the EU introduced the Energy Performance of Buildings Directive and this was enforced by all member states by 2006. The need to continually improve thermal performance has lead to member states implementing their own national initiatives, and from next year the National Standards Authority of Ireland will specify that all certified sandwich panel products comply with the incoming building regulations. The incoming building regulations stipulate that all sandwich panels achieve a U-value of 0.15 W/m2K, a reduction from the current value of 0.25 W/m2K. This is a significant challenge and requires that there be no significant heat loss through the panel. This paper presents the results of a collaborative project with a sandwich panel manufacturer whereby the thermal performance of a number of concrete panels was assessed. Each sandwich panel contained an inner concrete wythe of 150mm thickness, a 120mm layer of phenolic foam insulation and a 90mm thick outer layer of concrete. For structural reasons it is necessary to use connectors between the inner and outer concrete wythes, but these connectors have the potential to allow heat loss. In this study 2 connector types were used: 1 manufactured using FRP, the other with stainless steel. A control (non-structural) panel was manufactured containing no connectors. The thermal performance of each panel was assessed through experimental hot-box testing to determine U-values. This was complemented by a series of images taken using a thermal camera to show areas of heat loss. In addition the U-values were also determined using a theoretical numerical approach and a thermal finite element analysis (using MSC Patran) was conducted to determine the heat flux through the panel. The results showed that the connector type has a significant influence on the thermal performance of the sandwich panels, and that those containing steel connectors were not capable of providing the required U-value. The relative performance of the various panel types was consistent between analysis methods, as the finite element, the numerical and experimental approaches were in agreement. In addition, the heat losses observed through the thermal imaging camera were consistent with the heat losses predicted by the finite element analysis. It is proposed then that the use of numerical and finite element approaches has a valuable role in the design of thermally efficient sandwich panels. The experimental testing required is time consuming and requires significant effort. The analysis approach described above will make the design process more efficient and facilitate the construction of energy efficient buildings.

In a traditional deterministic assessment, a dynamic amplification factor (DAF) is applied to the static loading in order to account for dynamics. The codified DAF values are appropriately conservative in order to consider the wide range of structures and load effects to which they are applied. In the current analysis, a site specific assessment dynamic ratio (ADR) is calculated from direct measurement on an eighty year old steel truss railway bridge. The ADR is defined as the ratio of characteristic total stress to the characteristic static stress. The application of an ADR is a relatively new concept which has rarely been considered for railway bridges. An assessment performed on the bridge in question showed a decrease in the dynamic allowance when considering the site specific ADR, corresponding to a twenty-six percent decrease in the calculated stress. The measurements available were also used to derive a robust stochastic model for dynamic allowance which considered the correlation between DAF and stress level. The developed model was applied to a probabilistic assessment and resulted in a nine percent increase in reliability. © Civil-Comp Press, 2016.

Currently, the available decision support systems (DSS) rely on risk/vulnerability measures while interdependencies and their resilience in response to extreme environmental hazards are overlooked. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. As infrastructures are becoming more interdependent at some sectors, there is an increasing need for better management of these interactions and interdependencies. Conventional infrastructure management techniques aim to provide a high degree of reliability in design process and risk analysis is the commonly used technique in assessing the response to disastrous threats. However, there are limitations to risk assessment in which not all risks can be quantified due to the existence of emerging and unobserved threats and highly improbable events with high degree of uncertainty are dealt with poorly. To manage the infrastructure interdependencies and their interactions in response to disastrous events, in addition to holistic risk/vulnerability mitigation approach; there is a need for resilience-informed management system. This will establish the key components of existing CI network and will assess the sensitivity of these components to disastrous events and their capacity in coping with such events. Resilience informed management systems put the main emphasis on systems’ recoverability, resourcefulness and rapidity, in addition to its robustness. Unlike the available DSSs, RV-DSS provides a measure of network resilience in response to hazardous events, in addition to vulnerability measure. This measure provides a quantitative metric of the robustness, recoverability, rapidity and resourcefulness of the infrastructure network in response to environmental hazards. This paper presents the application of RV-DSS on a transport network case study, quantifying failure propagation due to inherent infrastructure interdependencies and streamlining strategic planning (e.g., winter preparedness in road and rail networks; use of smart technologies in structural health monitoring) by focusing on risk zones of infrastructure networks, and improve the resilience of infrastructure systems in response to low probability/high impact hazardous events.

Changes in the likelihood and severity of extreme weather events and climate-related disasters can result in the failure of critical infrastructure (CI) elements and networks. A Quantitative risk assessment of such failures is a core part of risk management protocols. In the RAIN project, funded by 7th Framework Programme, a systematic risk analysis framework is being developed which quantifies risks due to extreme events by explicitly considering the impacts of extreme weather events on critical infrastructure. In this paper, an overview of developed advanced risk assessment procedure quantifying multi-mode risks and the techniques required to assess the interaction between different hazardous events and various critical infrastructure systems is provided. In this study it is assumed that multi-risk scenarios refer to two main components: a. multi-hazard (i.e. the potential for one or more secondary hazard triggered a by primary hazard event) and b. multi-vulnerability (i.e., potential for failure propagation in critical infrastructure network(s)) scenarios. Descriptions of various multi-hazard and multi-vulnerability scenarios are provided and the approach required to quantify risk arising from each scenario is outlined and the application is illustrated for flash flooding case study in North of Italy.

A framework is presented for the assessment of the safety of a bridge deck under actual traffic loading using an enhanced Monte Carlo method which attempts to reduce computational cost while preserving the advantages of more conventional, computationally intensive, simulation. To generate the bridge loading scenarios, an extensiveWeigh-in-Motion (WIM) database is used to calibrate a sophisticated simulation model of two-directional traffic. Traffic and vehicle characteristics are generated from statistical distributions derived from measured traffic data. Two examples are used in this study to assess the usefulness and accuracy of the enhanced method. In the first, a simple example is used for which the exact theoretical probability of failure is available. Hence, the error in estimation can be assessed directly. In the second, 'long-run' simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. © 2013 Taylor & Francis Group, London.

To perform effectively, a Bridge Weigh-in-Motion (B-WIM) system requires accurate information on the location and speed of all axles on the bridge. In recent years, axle detection is by sensors under the bridge – so called Free-of-Axle-Detector or Nothing-On-Road (NOR) B-WIM. As axles pass over an axle detecting strain sensor, there is a peak in strain which can be detected by the data acquisition system. This approach works well for some bridges but there are challenges for beam-and-slab bridges where the beams are deep, a common form of construction in Alabama. The slabs in such bridges are therefore generally used for axle detection but the peaks in the slab strains are quite sensitive to the transverse position of the wheels over the beam. This paper describes a study into axle detection which tests alternative strategies for a range of bridge types and spans.

Assessment of highway bridge safety requires a prediction of the probability of occurrence of extreme load effects during the remaining life of the structure. While the assessment of the strength of an existing bridge is relatively well understood, the traffic loading it is subject to, has received less attention in the literature. The recorded traffic data are often limited to a number of days or weeks due to the cost of data collection. Studies in the literature have used many different methods to predict the lifetime maximum bridge load effect using a small amount of data, including fitting block maximum results to a Weibull distribution and raising maximum daily or maximum weekly distributions to an appropriate power. Two examples are used in this study to show the importance of the quantity of data in predicting the lifetime maximum distribution. In the first, a simple example is used for which the exact theoretical probabilities are available. Hence, the errors in estimations can be assessed directly. In the second, ‘long-run’ simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Results are presented for bidirectional traffic, with one lane in each direction, based on Weigh-in-Motion data from the Netherlands.

The recent extreme weather events in UK, Europe and around the world have raised issues about the organization and management of critical infrastructure subjected to any crisis. Lack of information on infrastructure management when subject to these disastrous events, along with embedded uncertainties of these disastrous events, will lead to disruptions to all connected critical infrastructure. Recently there has been a change in ideology where instead of relying on the ability to reduce risks induced by disastrous events, it is economically desirable to manage infrastructure network based on their resilience and their capacity in coping with these disruptions. This paper summarizes the work that is being implemented in developing a resilience-informed decision making system. Resilience-informed management system will provide means of quantifying the level of system bounce-back ability to all hazard scenarios in addition to their vulnerability. This study will eventually provide means of optimizing design and investment strategies for critical infrastructure owners and managers.

The recent extreme weather events in Europe and around the world have raised issues about the organization and management of critical infrastructure. There is uncertainty and a lack of information on how infrastructure should be managed when subject to these extreme events. The existence of chaos and uncertainty in these situations can result in disruptions to transport, power outages and in the most extreme instances, loss of life. The 7th Framework RAIN (Risk Analysis of Infrastructure Networks in response to extreme weather) project is addressing these issues, involving partners from Ireland, Belgium, Germany, Finland, Italy, Netherlands, Slovenia and Spain. The objective of the RAIN project is to provide an operational analysis framework to minimize the impact of major weather events in the EU. This paper summarizes the work that will be performed in one of the work packages of the RAIN project. This work package will examine the impact of critical infrastructure failure on society, security issues and the economy. Based on a risk analysis framework, a means of quantifying the level of risk will be established. The risk procedure will be benchmarked against case studies conducted on critical transport and operational tactical connections. The project outputs will contribute to the process of knowledge management used in the protection of Critical Infrastructure and will provide a basis for the development of decision support systems.

To assess the safety of an existing bridge, the traffic loads to which it may be subjected in its lifetime need to be accurately quantified. In this paper the 75 year characteristic maximum traffic load effects are found using a carefully calibrated traffic load simulation model. To generate the bridge loading scenarios, an extensive weigh in motion (WIM) database, from three different European countries, is used. Statistical distributions for vehicle weights, inter-vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for Monte Carlo simulations of traffic representing many years. An advantage of this “long-run” simulation approach is that it provides information on typical extreme traffic loading scenarios. This makes possible a series of nonlinear finite element analyses of a reinforced concrete bridge to determine the response to typical characteristic maximum loadings. Results of the nonlinear analyses are compared to the corresponding results using Eurocode and AASHTO load models.

In recent years, a variety of extreme weather events, including droughts, rain induced landslides, river floods, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions across Europe and worldwide. These events can have devastating impact on critical infrastructure systems. The 7th Framework RAIN project will address these issues, involving partners from Ireland, Belgium, Germany, Finland, Italy, Netherlands, Slovenia and Spain. In this project, the impact of critical infrastructure failure on society, on security issues and on the economy will be examined. Based on the impacts of the failures, quantifiable benefits (from a societal, security and economic standpoint) of providing resilient infrastructure will be identified. In this project, a means of quantifying the level of risk will be established, first due to single land transport mode failures, and second due to selected multi-mode-interdependent failure scenarios (e.g. failure of power stations result in failure of electrical train lines). This paper introduces the RAIN project and its goal of developing a methodology to create an advanced risk assessment procedure, including a probabilistic based approach, to derive a measurable indicator of risk.

A framework is presented for the assessment of the safety of a bridge deck, taking account of its 2-dimensional nature. Random field analysis is proposed to determine the spatial distribution of resistance probability but is not implemented in this paper. Monte Carlo simulation, calibrated using Weigh-in-Motion traffic data, is used to determine lifetime maximum distributions of bending moment throughout the bridge deck. The 2-dimensional approach is shown, for the example considered, to give much lower probabilities of failure than the alternative approach of considering points one by one.