TY - CONF A1 - Bayane, I. A1 - Long, Lijia A1 - Thöns, Sebastian A1 - Brühwiler, E. T1 - Quantification of the conditional value of SHM data for the fatigue safety evaluation of a road viaduct N2 - Fatigue safety verification of existing bridges that uses ‘‘re-calculation’’ based on codes, usually results in insufficient fatigue safety, triggering invasive interventions. Instead of “re-calculation”, Structural Health Monitoring (SHM) should be used for the assessment of the existing bridges. Monitoring systems provide data that can reduce uncertainties associated with the fatigue loading process and the structural resistance. The objective of this paper is to quantify the value of the SHM system implemented in a 60-years-old road viaduct to investigate its fatigue safety, through modeling of the fundamental decisions of performing monitoring in conjunction with its expected utility. The quantification of the conditional value of information is based on the decision tree analysis that considers the structural reliability, various decision scenarios as well as the cost-benefit assessments. This leads to a quantitative decision basis for the owner about how much time and money can be saved while the viaduct fulfills its function reliably and respects the safety requirements. The originality of this paper stands in the application of the value of information theory to an existing viaduct considering the fatigue failure of the system based on the monitoring data and the cost-benefit of monitoring method. T2 - 13th International Conference on Applications of Statistics and Probability in Civil Engineering CY - Seoul, South Korea DA - 26.05.2019 KW - Fatigue safety KW - Value of information PY - 2019 SP - 275 EP - 288 CY - Seoul, South Korea AN - OPUS4-50809 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia A1 - Döhler, Michael A1 - Thöns, Sebastian T1 - Damage detection and deteriorating structural systems N2 - This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented. The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the Information and its precision on structural system level, facilitating actions to ensure the structural integrity and facilitating to describe the structural system performance and its functionality throughout the service life. The structural system performance is described with its functionality, its deterioration and its behavior under extreme loading. The structural system reliability given the damage detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection Information is then calculated as the difference between the expected benefits and risks utilizing the damage detection information or not. With an application example of the developed approach based on a deteriorating Pratt truss system, the value of damage detection information is determined, demonstrating the potential of risk reduction and expected cost reduction. T2 - INFRASTAR Implementation day CY - BASt, Bergish Gladbach, Germany DA - 20-21.03.2018 KW - Value of information KW - Damage detetction system PY - 2017 AN - OPUS4-44586 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia A1 - Mai, Q. A. A1 - Sørensen, J. A1 - Thöns, Sebastian T1 - On the value of SHM information for offshore wind turbines N2 - Applications of Structural Health Monitoring (SHM) for the design and analysis of Offshore Wind Turbine (OWT) structures has gained much attention within the past few years. Being exposed to repeated cyclic wind and wave loads, OWTs are dynamically sensitive structures and can benefit from monitoring systems to predict time-dependent deterioration. This study focuses on the quantification of the value of SHM information on the maintenance of OWT structures, with emphasis on fatigue of welded joint. By utilizing the decision tree, structural reliability, SHM data, as well as the cost-benefit assessments, a value of information (VoI) analysis can be done to model the fundamental decision of whether the service life of an OWT foundation can be extended beyond the initial design life. The application is demonstrated on a butt weld of a monopile support structure of a 3 MW offshore wind turbine with a hub height of approximately 71m where the prior probability analysis is modelled as a probabilistic fatigue damage model based on S-N approach and designed wind data. The posterior probability of failure of welded joints is updated using the three-year measured oceanographic and one-year strain data. The expected value of SHM information can be found as the difference between the maximum utility obtained in posterior analysis with SHM information and the maximum utility obtained using only prior information. This work can provide insights on how much benefits can be achieved through SHM information, with practical relevance on reliability-based design optimization and fatigue life extension of OWT structures. T2 - INFRASTAR Training School, Nantes CY - Nantes, France DA - 08.04.2019 KW - Offshore wind turbines KW - SHM information KW - Wind turbine operator KW - Strain measurement PY - 2019 AN - OPUS4-48478 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia T1 - Quantification of the value of monitoring information for deteriorated structures N2 - A method to determine the structural and damage detection system influencing parameters is proposed based on the value of information analysis. The value of information analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of SHM for the structural integrity management during service life. First the influencing parameters of the structural system such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the damage detection system performance influencing parameters including number of sensors, sensor locations, measurement noise and the Type I error are investigated. The pre-posterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of information is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the higher the deterioration rate is, the more it is beneficial to do SHM. Furthermore, it shows that more sensors do not necessarily lead to a higher value of information; only specific sensor locations near the highest utilized components lead to a high value of information; measurement noise and the Type I error should be controlled and as small as possible. An optimal sensor employment with highest value of information is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies - before implementation - for similar bridges and to optimize the damage detection system settings and sensor configuration for minimum expected costs and risks. T2 - INFRASTAR Training School, Nantes CY - Nantes, France DA - 08.04.2019 KW - Damage detection KW - Value of information KW - Monitoring information PY - 2019 AN - OPUS4-48480 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia A1 - Thöns, Sebastian A1 - Döhler, M. T1 - The effects of SHM system parameters on the value of damage detection information N2 - This paper addresses how the value of damage detection Information depends on key Parameters of the Structural Health Monitoring (SHM) system including number of sensors and sensor locations. The Damage Detection System (DDS) provides the information by comparing ambient vibration measurements of a (healthy) reference state with measurements of the current structural system. The performance of DDS method depends on the physical measurement properties such as the number of sensors, sensor positions, measuring length and sensor type, measurement noise, ambient excitation and sampling frequency, as well as on the data processing algorithm including the chosen type I error for the indication threshold. The quantification of the value of Information (VoI) is an expected utility based Bayesian decision analysis method for quantifying the difference of the expected economic benefits with and without information. The (pre-)posterior probability is computed utilizing the Bayesian updating theorem for all possible indications. If changing any key parameters of DDS, the updated probability of system failure given damage detection information will be varied due to different indication of probability of damage, which will result in changes of value of damage detection information. The DDS system is applied in a statically determinate Pratt truss bridge girder. Through the analysis of the value of information with different SHM system characteristics, the settings of DDS can be optimized for minimum expected costs and risks before implementation. T2 - PhD day 2018 CY - BAM, Berlin, Germany DA - 31.05.2018 KW - SHM KW - Damage detection system KW - Value of information PY - 2018 AN - OPUS4-46192 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia T1 - Breaking the wall of value of monitoring information N2 - On 14 August 2018, the Morandi bridge in Genoa Italy collapsed, which caused 43 people to die and great losses. Here I am not going to talk about why it collapsed. But I want to say what we can do to prevent this kind of tragedy from happening. Any structures are like us, if we are sick, we go to doctors. But if structures are sick, how could we know? Yes, through structural health monitoring-SHM! SHM is process of implementing damage detection strategy, which is like a doctor giving us indication of the structure health status. However, the SHM is not like our health insurance which can be free. Some structure owners may be hesitated on investing large money before they see the VALUE. Although it is proved that SHM is beneficial, the existing literature doesn’t tell them: when to do the monitoring? How often to do that? Which SHM techniques to choose? After monitoring, what action to take? There are so many decisions needed to be made, that’s why here comes my research, which can help to answer all these questions by breaking the wall of value of monitoring information! So how do we do that? Here we introduce a decision tree. A Decision tree is a decision support tool that uses a tree-like graph or model to describe decisions and their possible outcomes, including how likely it will happen, how severe the consequences will be. Based on the probabilities and their respective costs and benefits, the value of every decision can be quantified. Based on the decision trees, different SHM strategies can be compared and the optimal strategy will be the one with highest value. After Knowing the value of SHM, we can improve the decision basis for design, operation and life-cycle integrity management of structures to reduce the risk, reduce the cost and extend service life benefits. So that everyone in society can benefit from a safer and more reliable environment! T2 - Falling Wall Labs Marie Curie Action 2018 CY - Brussels, Belgium DA - 24.09.2018 KW - Value of monitoring information PY - 2018 AN - OPUS4-46296 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Long, Lijia A1 - Alcover, I. F. A1 - Thöns, Sebastian T1 - Utility analysis for SHM durations and service life extension of welds on steel bridge deck N2 - Optimization of the duration of Structural Health Monitoring (SHM) campaigns is rarely performed. This article provides a utility-based solution to posteriorly determine: i) optimal monitoring Durations and ii) the extension of the service life of the welds on a steel bridge deck. The approach is Illustrated with a case study focusing on remaining fatigue life estimation of the welds on the orthotropic steel deck of the Great Belt Bridge, in Denmark. The identification of the optimal monitoring duration and the decision about extending the service life of the welds are modelled by maximizing the expected benefits and minimizing the structural risks. The results are a parametric analysis, mainly on the effect of the target probability, benefit, cost of failure, cost of rehabilitation, cost of monitoring and Discount rate on the posterior utilities of monitoring strategies and the choice of service life considering the risk variability and the costs and benefits models. The results show that the decision on short-term monitoring, i.e., 1 week every six months, is overall the most valued SHM strategy. In addition, it is found that the target probability is the most sensitive parameter affecting the optimal SHM Durations and service life extension of the welds. KW - Fatigue KW - Monitoring strategy KW - Orthotropic steel deck KW - Structural health monitoring KW - Utility and decision theory PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-521719 SN - 1573-2479 VL - 18 IS - 4 SP - 492 EP - 504 PB - Taylor Francis Online AN - OPUS4-52171 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia A1 - Alcover, I. F. A1 - Thöns, Sebastian T1 - Quantification of the posterior utilities of SHM campaigns on an orthotropic steel bridge deck N2 - This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic Counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management. T2 - The 12th International Workshop on Structural Health Monitoring CY - Stanford University, CA, USA DA - 10.09.2019 KW - SHM KW - Posterior utilities KW - Orthotropic steel bridge deck PY - 2019 SP - 265 EP - 274 AN - OPUS4-49174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Long, Lijia A1 - Alcover, I. F. A1 - Thöns, Sebastian T1 - Quantification of the posterior utilities of SHM campaigns on an orthotropic steel bridge deck N2 - This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic Counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management. T2 - IWSHM 2019, The 12th International Workshop on Structural Health Monitoring, Stanford, California, USA CY - Stanford, California, USA DA - 10.09.2019 KW - Quantification KW - SHM information KW - Steel bridge deck PY - 2019 SP - 1 EP - 9 AN - OPUS4-48481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Long, Lijia A1 - Anh Mai, Q. A1 - Morato, P. G. A1 - Dalsgaard Sorensen, J. A1 - Thöns, Sebastian T1 - Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures N2 - The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures. KW - Structural health monitoring KW - Offshore wind turbine KW - Monopile support structure KW - Value of information KW - Weld fatigue KW - Decision tree KW - Dynamic Bayesian Network PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-514098 VL - 10 IS - 159 SP - 1036 EP - 1046 PB - Elsevier Ltd. AN - OPUS4-51409 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -