Structural Safety最新文献

{"title":"Reliability-based quantification of the benefits of machine learning predictive models in seismic structural design and performance assessment","authors":"Muneera A. Aladsani,&nbsp;Henry V. Burton","doi":"10.1016/j.strusafe.2025.102596","DOIUrl":"10.1016/j.strusafe.2025.102596","url":null,"abstract":"<div><div>Machine Learning (ML) techniques have been used extensively in research within the field of structural engineering due to their high level of accuracy in predicting the behavior of different structural elements. In fact, the superior predictive performance relative to traditional statistical models is often suggested as the primary motivation for the adoption of ML models. However, the implications of such improvements in predictive accuracy in the design and performance of structural systems have not been studied. This paper presents a reliability-based investigation of the tangible benefits provided by ML models in terms of structural design and performance. To quantify these benefits, the increase in predictive accuracy is interpreted as a reduction in epistemic uncertainty. The specific focus is on a predictive model that estimates the drift capacity of reinforced concrete shear walls (RCSWs) with special boundary elements. The accuracy of an extreme gradient boosting (XGBoost) model relative to a basic linear regression equation is quantified in terms of reduced epistemic uncertainty. Then, using 36 RCSW archetype buildings, a Monte Carlo-based procedure is implemented to evaluate the implication of the improved predictive accuracy to seismic design and performance. The study provides insights into how much improvement in accuracy (i.e., ML relative to traditional model) is needed to have a tangible effect on the seismic design and performance.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102596"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical-informed random field technique for virtual modelling based building probabilistic vulnerability assessment","authors":"Zhiyi Shi ,&nbsp;Yuan Feng ,&nbsp;Mark G. Stewart ,&nbsp;Wei Gao","doi":"10.1016/j.strusafe.2025.102595","DOIUrl":"10.1016/j.strusafe.2025.102595","url":null,"abstract":"<div><div>Developing a probabilistic vulnerability assessment framework for bushfire-prone buildings is a critical measure to reduce bushfire-induced risks to life safety and economic losses to an acceptable level. A reliable assessment approach should include multiple probability-based macro indicators by considering their inherent uncertainties. These macro indicators can incorporate the efficiency of bushfire-damaged transportation network at specified moments, the geographical position of buildings, among others. A Physics-Informed Random Field-Virtual Modelling (PIRF-VM) framework for probabilistic vulnerability assessment of bushfire-prone buildings in large-scale bushfire incidents is proposed. The PIRF generates a random field-based, multi-physical information-fusion model for the simulation of bushfire spread in a large-scale approximate natural environment. The integrated physical information includes the spatially varying vegetation characteristics, the Digital Elevation Model (DEM)-based terrain, the terrain-shaped time-dependent wind field, the geographical coordinates of roads and buildings. To mitigate the computational burden posed by stochastic bushfire simulations in PIRF, the VM is introduced. It can establish an explicit functional relationship between input physical information and output responses of interest, such as the remaining time for bushfire reaching a specified location. As a result, for any new input physical information, the output responses can be directly predicted without time-consuming simulations. Benefiting from the efficient predictions of the PIRF-VM, several probability-based macro indicators are simultaneously considered when assessing the probabilistic vulnerability for bushfire-prone buildings in large-scale bushfire incidents. The Australian community of Cowan serves as an example to illustrate the practical application of the proposed scheme, demonstrating potential in constructing more bushfire-resilient communities in the face of bushfire hazards.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102595"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic life-cycle assessment of landslides exposed to both rainfall under nonstationary climate change effects and earthquakes","authors":"Zhengying He ,&nbsp;Mitsuyoshi Akiyama ,&nbsp;Abdul Kadir Alhamid ,&nbsp;Dan M. Frangopol ,&nbsp;Yu Huang","doi":"10.1016/j.strusafe.2025.102599","DOIUrl":"10.1016/j.strusafe.2025.102599","url":null,"abstract":"<div><div>Combined effects of rainfall and seismic hazards pose significant threats to structures and infrastructure systems. Additionally, climate change is projected to impact the intensity and frequency of future rainfall, increasing the likelihood of landslides. However, evaluating long-term landslide probability under the combined effects of rainfall and seismic hazards, while considering nonstationary climate change, presents significant challenges due to the distinct characteristics of their occurrence processes. This study introduces a novel framework for probabilistic life-cycle landslide assessment that systematically integrates climate change effects on rainfall hazard along with seismic hazard. Probabilistic nonstationary rainfall and seismic hazard models are developed by leveraging stochastic renewal process theory based on occurrence probability and the associated hazard intensity distribution. Slope fragility assessments are conducted for four event scenarios: individual rainfall, individual earthquake, rainfall followed by an earthquake, and an earthquake followed by rainfall, using seepage and equivalent linear analysis through Monte Carlo simulation. Finally, using the total probability theorem, life-cycle landslide probability is numerically evaluated by convolving nonstationary rainfall and seismic hazards with slope fragilities. An illustrative example is provided by applying the proposed framework to a slope in Hiroshima city, Japan, to explore how the combined effects between nonstationary rainfall and seismic hazards impact life-cycle landslide probability.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102599"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A phase-control-based method for the simulation of homogeneous random fields of fluctuating wind speed","authors":"Jia-Hang Lyu ,&nbsp;Jian-Bing Chen ,&nbsp;Pol D. Spanos ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102593","DOIUrl":"10.1016/j.strusafe.2025.102593","url":null,"abstract":"<div><div>The simulation of stochastic processes, and of time-variant random fields finds extensive applications across various scientific and engineering domains. Despite the existence of a variety of methods, including the well-developed spectral representation method, it is still necessary to study the representation of the correlation structure of time-variant random fields. This paper proposes a phase control method for simulating correlated stochastic processes and spatiotemporal random fields. First, by introducing an auxiliary random phase angle and controlling its amplitude, the correlation of two stochastic processes can be precisely reproduced by introducing the auxiliary phase angle to the original process. Further, for time-variant random field simulation, the correlation structure of the random field is converted into that of the random phase angle field, thereby making it possible for the random field simulation either by phase shifting from a single process or using the spectral representation method in a decoupled manner. The effectiveness of the proposed method is validated by two numerical examples of fluctuating wind field simulation. This method provides an alternative perspective on the correlation structure of random fields and could be used for conditional simulation of random fields in future work.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102593"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-output stochastic emulation with applications to seismic response correlation estimation","authors":"Sang-ri Yi ,&nbsp;Alexandros A. Taflanidis","doi":"10.1016/j.strusafe.2025.102578","DOIUrl":"10.1016/j.strusafe.2025.102578","url":null,"abstract":"<div><div>Stochastic emulation techniques represent a specialized surrogate modeling branch that is appropriate for applications for which the relationship between input and output is stochastic in nature. Their objective is to address the stochastic uncertainty sources by directly predicting the output distribution for a given input. An example of such application, and the focus of this contribution, is the estimation of structural response (engineering demand parameter) distribution in seismic risk assessment. In this case, the stochastic uncertainty originates from the aleatoric variability in the seismic hazard description. Note that this is a different uncertainty-source than the potential parametric uncertainty associated with structural characteristics or explanatory variables for the seismic hazard (for example, intensity measures), that are treated as the parametric input in surrogate modeling context. The key challenge in stochastic emulation pertains to addressing heteroscedasticity in the output variability. Relevant approaches to-date for addressing this challenge have focused on scalar outputs. In contrast, this paper focuses on the multi-output stochastic emulation problem and presents a methodology for predicting the output correlation matrix, while fully addressing heteroscedastic characteristics. This is achieved by introducing a Gaussian Process (GP) regression model for approximating the components of the correlation matrix, and coupling this approximation with a correction step to guarantee positive definite properties for the resultant predictions. For obtaining the observation data to inform the GP calibration, different approaches are examined, relying-or-not on the existence of replicated samples for the response output. Such samples require that, for a portion of the training points, simulations are repeated for the same inputs and different descriptions of the stochastic uncertainty. This information can be readily used to obtain observation for the response statistics (correlation or covariance in this instance) to inform the GP development. An alternative approach is to use as observations noisy covariance samples based on the sample deviations from a primitive mean approximation. These different observation variants lead to different GP variants that are compared within a comprehensive case study. A computational framework for integrating the correlation matrix approximation within the stochastic emulation for the marginal distribution approximation of each output component is also discussed, to provide the joint response distribution approximation.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102578"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating risk perceptions in a value of information framework using cumulative prospect theory","authors":"Zaid Y Mir Rangrez ,&nbsp;Jayadipta Ghosh ,&nbsp;Colin Caprani ,&nbsp;Siddhartha Ghosh","doi":"10.1016/j.strusafe.2025.102573","DOIUrl":"10.1016/j.strusafe.2025.102573","url":null,"abstract":"<div><div>Value of information (VoI) analysis provides a framework that can be used to decide on an optimal monitoring strategy, to carry out an efficient maintenance of civil infrastructure. Existing VoI frameworks adopt utility functions to characterize the risk appetite of an asset manager based on expected utility theory (EUT). However, these utility functions cannot predict the decision choices under uncertainty resulting from failure risk perceptions. Cumulative prospect theory (CPT) is a comprehensive model for characterizing an asset manager’s risk appetite and perception. CPT captures both, the preference for different action outcomes using a value function and corresponding risk perceptions exhibited by an asset manager using a probability weight function. The present study proposes a CPT-based VoI framework which integrates risk perceptions and appetite within the VoI analysis. The proposed framework is implemented to investigate the sensitivity of the resulting expected VoI and the monitoring decisions to risk perception profiles. It is observed that the VoI is sensitive to the risk perception profile of an asset manager. An in-depth analysis of the decision patterns reveal that the risk profile affects the choice of prior optimal action that in turn dictates which type of posterior actions contribute positively or negatively towards the cost savings when referenced to the cost of prior optimal action. Based on these finding, the paper recommends to calibrate an asset manager’s risk perception profile to predict the decisions that an asset manager perceives as optimal for a given failure risk, and to evaluate the expected VoI resulting from such decisions.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102573"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Memoriam Alfredo Hua-Sing Ang July 4, 1930 – October 14, 2024","authors":"Armen Der Kiureghian,&nbsp;Bruce R. Ellingwood","doi":"10.1016/j.strusafe.2025.102594","DOIUrl":"10.1016/j.strusafe.2025.102594","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102594"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient reliability analysis for offshore wind turbines: Leveraging SVM and augmented oversampling technique","authors":"Xukai Zhang,&nbsp;Arash Noshadravan","doi":"10.1016/j.strusafe.2025.102597","DOIUrl":"10.1016/j.strusafe.2025.102597","url":null,"abstract":"<div><div>This study develops an efficient reliability assessment method designed to optimize maintenance strategies for Offshore Wind Turbines (OWT), aiming for significant cost savings through reduced maintenance frequency and enhanced efficiency. Effective cost management requires a robust and accurate approach for reliability-based lifecycle management. Therefore, this paper introduces an improved predictive maintenance method, grounded on the reliability-based failure probability of OWT systems. To augment computational efficiency and diminish computational time, a surrogate model is proposed for the estimation of failure probability. This surrogate model integrates the classification strengths of Support Vector Machine (SVM) with an augmented Synthetic Minority Oversampling Technique (SMOTE), specifically adapted for extremely imbalanced data. The study’s contributions are twofold: firstly, it develops a novel reliability-based predictive maintenance method allowing for the quantitative assessment of OWTs’ current conditions; secondly, it presents a surrogate model adept at managing extreme data imbalance, thereby enhancing prediction accuracy. The effectiveness of the surrogate model is validated through a case study under two distinct weather conditions. The proposed predictive maintenance method serves as an efficient and effective tool for improved maintenance planning for OWTs.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102597"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The probabilistic inverse problem and its solving method based on probability density evolution theory and convex optimization algorithms","authors":"Yuhan Zhu,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2025.102600","DOIUrl":"10.1016/j.strusafe.2025.102600","url":null,"abstract":"<div><div>A probabilistic inverse problem-solving method based on the framework of Probability Density Evolution Theory and convex optimization algorithms is proposed. This method reformulates the identification of the random source as a quadratic programming problem with linear constraints, identifying the probability density function of the random source in a physical stochastic system even when the distribution type of the random source is entirely unknown. Through singular value decomposition of the quadratic matrix, an error analysis is performed, revealing that the solvability of the probabilistic inverse problem fundamentally depends on the injectivity of the mapping from the random source space to the response space. Case studies confirm that the proposed method is not sensitive to prior information and does not require any predefined assumptions about the distribution type. Meanwhile, it can preliminarily determine whether the inverse problem is solvable before the computational process begins.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102600"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serviceability limit state target reliability for concrete structures","authors":"Andrew Way ,&nbsp;Frederik Bakker ,&nbsp;Dirk Proske ,&nbsp;Celeste Viljoen","doi":"10.1016/j.strusafe.2024.102572","DOIUrl":"10.1016/j.strusafe.2024.102572","url":null,"abstract":"<div><div>The balance between safety and economy, referred to as target reliability, forms the basis of modern structural design. Target reliability is determined by economic minimisation, either directly through generic cost optimisation or by back calibration to existing practice. However, the currently codified annual target reliability indices for serviceability limit state (SLS), differ by as much as <span><math><mrow><mi>Δ</mi><mi>β</mi><mo>=</mo><mn>1.6</mn></mrow></math></span> between those from generic cost optimisation and back calibration. Various assumptions are made in the generic cost optimisation which may not be appropriate to determine SLS target reliability. Target reliability from back calibration is likely to be closer to actual SLS failure rates, however, no literature exists which details the process or rationale by which the back calibration was performed. It is therefore uncertain if either of these methods produce cost optimal SLS target reliability. This research aims to evaluate currently codified SLS target reliability for cost optimality. SLS failure costs from existing research and engineering practice are used with an amended cost optimisation procedure which overcomes the deficiencies identified in the generic formulation to specifically determine SLS target reliability. The amended cost optimisation also considers parameter variation and decision parameter form for typical SLS cases. Results indicate that overall, the target reliability indices for annual irreversible SLS from back calibration to existing practice (<span><math><mrow><mi>β</mi><mo>=</mo><mn>2.9</mn></mrow></math></span>) represents the range of considered SLS cases (<span><math><mrow><mn>2.5</mn><mo>≤</mo><mi>β</mi><mo>≤</mo><mn>3.3</mn></mrow></math></span>) well, whereas those from generic cost optimisation are notably lower (<span><math><mrow><mn>1.3</mn><mo>≤</mo><mi>β</mi><mo>≤</mo><mn>2.3</mn></mrow></math></span>). In some cases, target reliability varied sufficiently from <span><math><mrow><mn>2.9</mn></mrow></math></span> to warrant adjustments being made for better cost optimality.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102572"},"PeriodicalIF":5.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}