Professor Marios Chryssanthopoulos
Marios joined the University of Surrey in June 2000 as Professor of Structural Systems. He originally trained as a Naval Architect at the University of Newcastle and MIT, and then read for a PhD in Structural Engineering at Imperial College where he was also employed as Lecturer and British Steel Reader in Structures from 1989 until 2000. Before embarking on an academic career, he worked in industry with Flint and Neill on the design of aluminium bridges and with Det norske Veritas on the reliability of offshore structures. His research focuses on risk-based performance of structures and infrastructure systems and the development of decision support tools for asset management, for which he has been funded by EPSRC, the European Union and industry.
He has published over 150 scientific articles and several book chapters and has lectured widely in the UK and overseas, including a fellowship in Japan sponsored by the Japan Society for the Promotion of Science. He has acted as the convenor of an international working group on structural reliability and probabilistic design, operating under the auspices of the Joint Committee for Structural Safety (JCSS) and has served on national and European advisory and codification committees, including the UK Standing Committee on Structural Safety (SCOSS), the Eurocode 3 Drafting Panel on Shell Structures and the working group on the revision of ISO2394: General Principles on the Reliability of Structures.
Marios has served as the external examiner of undergraduate and post-graduate programmes at the Universities of Aberdeen, Edinburgh, Newcastle and Trinity College Dublin, and has been a PhD examiner in over 20 universities both in the UK and overseas. He is an editorial board member of Structural Safety (Elsevier), Journal of Earthquake Engineering and Structure and Infrastructure Engineering (Taylor and Francis).
These relate to how randomness, variability and uncertainties in human and organisational factors influence the response of structural and infrastructure systems subject to natural and man-made hazards, and the development of risk-based performance standards. Over the past twenty years, a wide range of experimental, analytical and design-orientated studies have been undertaken, encompassing steel and fibre reinforced composite plates and shells, concrete and steel building frames, as well as metallic, concrete and FRP bridges. Increasingly, research has been directed towards the integration of advanced structural engineering and structural health monitoring into consequence analysis and loss estimation in support of risk-informed decision-making tools for structural systems and infrastructure networks. In the course of these investigations, he has supervised over 25 PhD theses and numerous MSc dissertations, has collaborated widely with industry and academics, and has co-authored over 150 publications in archival journals and international conference proceedings; examples of recent research projects include:
• Probabilistic modelling of performance profiles and life-cycle assessment • Fragility, damage and loss estimation for buildings under seismic loading • Spatial variability of material properties and deterioration processes • Imperfection sensitivity and damage tolerance of steel shell structures • Structural Health Monitoring of metallic bridges • Repair of metallic structures using FRP materials • Reliability-based fatigue life prediction of bridges and offshore structures
- Introduction to Structural Design, 1st year Civil Engineering, 2004 - onwards. - Structural Analysis (Plates/Stability), 3rd year Civil Engineering, 2010 – onwards. - Structural Safety and Reliability, MSc in Bridge/Civil/Structural Engineering, 2000 - onwards. - Bridge Management, MSc in Bridge/Civil/Structural/Bridge Engineering, 2000 – onwards. - Advanced Composites in Construction, MSc in Bridge/Civil/Structural Engineering, 2003 - 09. - Bridge Deck Loading, MSc in Bridge/Civil/Structural Engineering, 2012 - onwards.
From 2001 until 2004 Marios acted as the Director of the Engineering Materials and Structures Research Centre, comprising 10 academics and over 30 research students and assistants. Subsequently acted as Deputy Director of the Centre for Materials, Surfaces and Structural Systems, a larger grouping which included chemists and materials scientists, as well as civil and mechanical engineers. In 2006, he was appointed to the RAE Task Group for the School of Engineering, which prepared the submission to General Engineering for RAE2008. From October 2007 until November 2012, he served as Head of the Division of Civil, Chemical and Environmental Engineering, one of the largest academic units in the Faculty of Engineering and Physical Sciences.
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.
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.
•Surface roughness of corroded wires, subjected to an accelerated corrosion method, was measured using a 3D laser scanner.•Spatial characteristics of area loss and pit depth were quantified on nominally identical corroded specimens.•Different corrosion indicators were specified in order to explore correlations with fatigue performance.•The maximum area loss was better correlated, compared to maximum pit depth, with the breakage position.•Effect of corrosion was quantified through the estimation of the coefficients in the S-N relationships. This study investigates the fatigue performance of corroded bridge wires by providing characterisation of the corrosion effects through non-contact surface mapping and by undertaking fatigue tests under constant amplitude loading. For the corrosion level investigated, the breakage position in corroded wires was found to be better correlated with maximum area loss rather than pit depth. Using also test results for un-corroded wires, S-N relationships were determined for both that set and the corroded wire set created in this study. The effect of corrosion was quantified through the estimation of the coefficients for both the mean and the design S-N curves.
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.
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.
Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.
A vector-valued intensity measure is presented, which incorporates a relative measure represented by the normalized spectral area. The proposed intensity measure is intended to have high correlation with specific relative engineering demand parameters, which collectively can provide information regarding the damage state and collapse potential of the structure. Extensive dynamic analyses are carried out on a single-degreeof- freedom system with a modified Clough–Johnston hysteresis model, using a dataset of 40 ground motions, in order to investigate the proposed intensity measure characteristics. Response is expressed using the displacement ductility, and the normalized hysteretic energy, both of which are relative engineering demand parameters. Through regression analysis the correlation between the proposed intensity measure and the engineering demand parameters is evaluated. Its domain of applicability is investigated through parametric analysis, by varying the period and the strain-hardening stiffness. Desirable characteristics such as efficiency, sufficiency, and statistical independence are examined. The proposed intensity measure is contrasted to another one, with respect to its correlation to the engineering demand parameters. An approximate procedure for estimating the optimum normalized spectral area is also presented. It is demonstrated that the proposed intensity measure can be used in intensity-based assessments, and, with proper selection of ground motions, in scenario-based assessments.
Although the majority of creep models are comprehensive and up-to-date, there is a lack of consensus in their utilisation due to substantial scatter in their predictions, even when comparisons are made under well-controlled conditions. On one hand, creep entails complex phenomena that depend on several factors and, on the other hand, these models are typically utilised on a deterministic basis without fully incorporating information related to random input variability. In this paper, a methodology is proposed, based on Bayesian updating methods, for creep deformation prediction by combining prior model distributions obtained through Monte Carlo simulation with in-situ measurements obtained from concrete specimens. Both single point-in-time and sequential updating approaches are formulated and contrasted in the context of site data collected over a period of about six years. For the specific structure examined, the sequential updating method offers advantages in terms of the estimated variability of future predictions. The proposed methodology is suitable for quantifying the value of monitoring information, as demonstrated by considering the change in prediction variability against the length of observation period.
Structural Health Monitoring Systems (SHMS) are increasingly present in most modern long-span bridges. Those systems can be used to better assess the performance of structures by reducing the uncertainties associated with deterioration modelling. This can potentially lead to a reduction of the operational costs. Despite their promise and potential, a gap still remains between the outcomes of those systems and practical bridge management decisions. As a result, huge amounts of data can be continuously collected which are not readily useable, thus being of reduced interest in practical terms. Methodologies which integrate SHMS within Bridge Management Systems (BMS), need to be developed to address asset management issues traditionally informed by visual inspections and scarce Non Destructive Tests (NDT). This integration should overcome the shortcomings of current approaches and exploit the advantages offered by modern sensor technologies. The present paper reviews the different uses of SHMS on long-span bridges. The motivation of using SHMS to inform and improve bridge management decisions is presented. The interest of a local monitoring approach targeting selected structural components is highlighted. The need of a combined approach between traditional inspection techniques, NDT and monitoring is justified in terms of spatial and temporal coverage. The relevance of probabilistic approaches to assess and update structural performance indicators is outlined. The case of the Great Belt Bridge (Denmark) is described to illustrate the use of SHMS on long-span bridges, together with an overview of ongoing research.
A modelling platform based on regression analysis is developed as a novel approach to structural health monitoring of welded joints of orthotropic bridge steel decks. Monitoring outcomes from the Great Belt Bridge (Denmark) are used to develop regression models following a weighted least squares approach to characterize the normal correlation pattern between environmental conditions (daily-averaged pavement temperatures), operational loads (daily-aggregated heavy traffic counts) and a strain-based performance indicator. The developed models can be used within a structural health monitoring–based asset management framework for performance assessment (i.e. diagnosis of structural performance changes) and performance prediction (i.e. prognosis of structural performance leading to service life estimates). The main novelty of the work presented consists of the development of an algorithm based on statistical control charts related to the prediction bands of the regression models. The algorithm enables the interpretation of new monitoring data and the identification of potentially abnormal behaviours via outlier detection, as part of an envisaged ‘real-time’ performance assessment application. The proposed approach to outlier detection through structural health monitoring is finally illustrated considering actual monitoring outcomes from the bridge. This highlights the applicability of the developed modelling platform and contributes to bridging the gap between monitoring data and monitoring-based information that can lead to more effective asset management decisions.
A novel methodology is presented for probabilistic fatigue life prediction of welded joints in orthotropic bridge steel decks. Monitoring data were used to specify time-series model parameters for the main drivers of fatigue damage in such structures, namely pavement temperatures and heavy traffic intensities, which influence the stress range distributions at critical locations. Polynomial regression models were developed to quantify the relationship between fatigue loading, derived using S-N principles from strain measurements at welded joints, with pavement temperatures and heavy traffic counts. The different models were integrated within a fatigue reliability framework, in which the uncertainties arising from material properties and fatigue damage at failure were modelled via random variables. A Monte Carlo scheme was then deployed to predict S-N fatigue damage using the fatigue loading regression models and simulated time-series of heavy traffic and pavement temperatures. Thus, fatigue reliability profiles were generated, which account for different scenarios in terms of future changes in traffic and pavement temperature. The proposed methodology was illustrated considering actual monitoring outcomes from the Great Belt Bridge (Denmark) with reliability profiles developed for both ‘baseline’ and ‘adverse’ scenarios in the context of asset integrity management. The combined effect of higher temperature and heavy traffic levels was shown to result in considerable reductions in fatigue reliability, with a commonly used threshold being reached up to 40 years earlier compared to the baseline ‘no change’ scenario. However, this reduction was not uniform for all the fatigue details considered, emphasizing the importance of monitoring different locations, based on a thorough understanding of the fatigue behaviour of the orthotropic steel deck.