Structural Safety最新文献

{"title":"An environment-driven basin scale tropical cyclone model","authors":"Feng Hu ,&nbsp;Qiusheng Li ,&nbsp;Xu Hong","doi":"10.1016/j.strusafe.2024.102480","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102480","url":null,"abstract":"<div><p>This paper presents an environment-driven tropical cyclone (TC) model for the Western North Pacific basin, which comprises a revised Poisson regression genesis model, a tailored beta-advection track model, and a fast intensity model. The TC model reproduces the temporal and spatial distributions of genesis events, the motion pattern of tracks, as well as the intensity evolutions along tracks. Risk analyses for Hong Kong and along the southeast coastline of mainland China demonstrate that this model can simulate extreme TC events with high fidelity. And the Gaussian mixture model outperforms the Frank Copula in approximating the joint distributions of the annual maximum wind speeds and the corresponding wind directions. This model is driven by a set of environmental variables including relative vorticity, relative humidity, sea surface temperature, vertical wind shear, potential intensity, sub mixed layer depth stratification, mixture layer depth and so on. This enables the model to not only reproduce historical records, but also make predictions for future TC behaviors under climate change with combination of global climate models. Besides, the computational efficiency of the TC model is comparable to traditional purely statistical models. The proposed model can also be coupled with other natural hazard models to conduct multi-hazard analysis.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102480"},"PeriodicalIF":5.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140824808","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":"LRFD methodology for river embankments against non-stationary flooding under climate change","authors":"Abdul Kadir Alhamid ,&nbsp;Mitsuyoshi Akiyama ,&nbsp;Zhengying He ,&nbsp;Putri Syahidah Firdaus ,&nbsp;Dan M. Frangopol","doi":"10.1016/j.strusafe.2024.102477","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102477","url":null,"abstract":"<div><p>Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102477"},"PeriodicalIF":5.8,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000481/pdfft?md5=b07bc6b4b752f32472f5e9ae685c59b4&pid=1-s2.0-S0167473024000481-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822711","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":"Global sensitivity analysis of the maximum live load and its applications","authors":"Chi Xu ,&nbsp;Jun Chen ,&nbsp;Jie Li","doi":"10.1016/j.strusafe.2024.102476","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102476","url":null,"abstract":"<div><p>The design live loads are determined by the probability distribution of the maximum live load, which is influenced by the amplitudes and time intervals of various sustained and extraordinary loads. If the relative impact of different input variables on the maximum can be clarified, more targeted load surveys and modeling can be achieved. However, there is currently no global sensitivity analysis that simultaneously considers all input variables. In this study, the probability density function of the maximum live load is determined using the load coincidence principle and probability density evolution method. The relative entropy is employed as a measure for conducting a global sensitivity analysis across five common building occupancy types. The results indicate a significant imbalance in the impact of different input variables. The load amplitudes have a much greater effect than the time intervals. Among various load amplitudes, those related to the extraordinary loads often have the most significant impact. Regarding the time intervals, the occurrence intervals corresponding to the extraordinary loads caused by furniture stacking and normal crowding consistently have the least influence. For the time intervals with minimal impact, it is suggested to treat them as deterministic values in the live load modeling. This treatment has a negligible impact (not exceeding 10%) on the mean and upper fractile of the maximum, which are generally used for design in load codes.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102476"},"PeriodicalIF":5.8,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140843739","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":"Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks","authors":"Denny Thaler ,&nbsp;Somayajulu L.N. Dhulipala ,&nbsp;Franz Bamer ,&nbsp;Bernd Markert ,&nbsp;Michael D. Shields","doi":"10.1016/j.strusafe.2024.102475","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102475","url":null,"abstract":"<div><p>We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102475"},"PeriodicalIF":5.8,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000468/pdfft?md5=25924e8f11cfb879db99320b7ba3054f&pid=1-s2.0-S0167473024000468-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649316","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":"Development of methods of structural reliability","authors":"Bruce Ellingwood ,&nbsp;Marc Maes ,&nbsp;F. Michael Bartlett ,&nbsp;Andre T. Beck ,&nbsp;Colin Caprani ,&nbsp;Armen Der Kiureghian ,&nbsp;Leonardo Dueñas-Osorio ,&nbsp;Neryvaldo Galvão ,&nbsp;Robert Gilbert ,&nbsp;Jie Li ,&nbsp;Jose Matos ,&nbsp;Yasuhiro Mori ,&nbsp;Iason Papaioannou ,&nbsp;Roger Parades ,&nbsp;Daniel Straub ,&nbsp;Bruno Sudret","doi":"10.1016/j.strusafe.2024.102474","DOIUrl":"10.1016/j.strusafe.2024.102474","url":null,"abstract":"<div><div>The growth of structural reliability theory and applications, along with a recognition of its role in guiding the structural engineering profession in addressing some of the most important issues in design of the built environment, represents one of the key engineering achievements during the past five decades. Structural reliability provides a unifying framework for managing uncertainties affecting performance of structures and a quantitative link between the practice of structural engineering and its social consequences. Such links perhaps are most obvious in probability-based codified design and performance evaluation but there are numerous other applications, which are summarized in this special issue. As the field has matured, researchers in reliability have worked with structural engineers to elevate both the practice of structural engineering and the quality of research to levels that otherwise would not have been possible. The Joint Committee on Structural Safety has played a central role in this historic development and it will inspire future opportunities for the reliability community to build upon past successes to improve structural engineering and construction practices. This paper surveys the key theoretical developments and milestones that enable these opportunities.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102474"},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140788037","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":"Interpretation of probability in structural safety – A philosophical conundrum","authors":"Ton Vrouwenvelder ,&nbsp;André Beck ,&nbsp;Dirk Proske ,&nbsp;Michael Faber ,&nbsp;Jochen Köhler ,&nbsp;Matthias Schubert ,&nbsp;Daniel Straub ,&nbsp;Max Teichgräber","doi":"10.1016/j.strusafe.2024.102473","DOIUrl":"10.1016/j.strusafe.2024.102473","url":null,"abstract":"<div><div>The term probability is essential in the domain of structural safety and yet its interpretation is, even after more than 50 years of application, still a subject of discussion. For instance, the probability of failure of structures belonging to the same cohort for a specific period of time, is often understood in a pure frequentist way as an observable average number of failure events for that period and portfolio. By contrast, the Bayesian interpretation considers probability as a degree of belief and a reflection of the state of information to the best belief or knowledge of the decision maker. In the field of structural reliability, depending on the type of decision problem, probabilities are often referred to as nominal (or notional) measures of uncertainty to emphasize that these values are conditional on a model and available observations. Probabilistic methods then serve primarily to undertake the book-keeping required to assign probabilities to different possible outcomes or decisions in consistency with models, available observations and other relevant information. This paper discusses the background of these interpretations and the degree to which correspondence between reliability calculations and observations of failures can be expected and/or achieved. Recommendations corresponding to the JCSS line of thinking will be summarized in <span><span>Section 8</span></span>.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102473"},"PeriodicalIF":5.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763725","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 baseline approach for probabilistic blast risk analysis of building cladding under external explosions","authors":"Orestis Ioannou,&nbsp;Georgios Rigoutsos,&nbsp;Dimitrios Vamvatsikos,&nbsp;Charis J. Gantes","doi":"10.1016/j.strusafe.2024.102472","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102472","url":null,"abstract":"<div><p>The state of the practice in blast-resistant applications against explosions is to design the structural components for a prescribed combination of explosive mass and location, namely the design basis threat. In this context, the blast source is represented by specific scenarios, mostly associated with expert judgement, rating systems or code provisions. While offering a useful basis for practical applications, the level of detail can be significantly enhanced within a probabilistic framework for risk assessment. In research practice, plenty simplified probabilistic approaches have been proposed on external explosions in order to perform risk assessment. A rigorous methodology for such an assessment is presented herein, using tools and techniques derived from seismic risk-assessment applications. Specifically, the mean annual frequency of different explosive mass hazards is represented via a recurrence law, while the potential locations are modelled as a 2D spatial distribution, accounting for the various layers of defense that exist around the structure, i.e., perimeter protection, landscape, public or private spaces, etc. The methodology is finally substantiated with the case study of a typical building subjected to potential blast loadings from external aggressors.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102472"},"PeriodicalIF":5.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632652","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":"Adaptive sampling based estimation of small probability of failure using interpretable Self-Organising Map","authors":"Deepanshu Yadav,&nbsp;Kannan Sekar,&nbsp;Palaniappan Ramu","doi":"10.1016/j.strusafe.2024.102470","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102470","url":null,"abstract":"<div><p>Structural and multidisciplinary design under uncertainty for high reliability or equivalently small probability of failure is a challenging task owing to the high computational cost associated with generating the samples at the extreme (tail) of the underlying distribution. Among other approaches, statistics of extremes based techniques are usually suitable for small probability estimation. However, typically only 10% of the samples generated that correspond to the tail of the distribution are used for probability estimation. If apriori information about regions in the design space that corresponds to the tail is available, additional samples in the identified region permit better tail fit and hence better probability estimation. In the current work, we propose iSOM (interpretable Self-Organising Map) to identify region/s in the design space, that corresponds to the extremes. An initial sample is used to map (visualize) the limit state function and random/design variables using iSOM which permits the designer to identify the region(s) that corresponds to the tail of the response. Adaptive sampling is performed in the identified region of interest to obtain additional samples. Next, the cumulative distribution function (CDF) of the response using initial as well as adaptive samples is evaluated for probability estimation. The effectiveness of the proposed approach is evident from its successful implementation on benchmark examples, real-world engineering examples, and a multi-objective reliability-based design optimization (MORBDO) case. The proposed method showcases the capability of iSOM to perform adaptive sampling for limit-state functions characterized by non-linearity and multiple modes. iSOM-enabled sampling in conjunction with log-TPNT provides better estimates of small failure probabilities than log-TPNT alone. The results from the proposed approach is compared with results from state-of-the-art (SOTA) sampling and surrogate-based techniques. For a given number of limit state evaluations, the proposed approach estimates probabilities of the order 1e−4, with lesser variance, compared to other SOTA approaches. Hence, the proposed approach is likely to encourage further research into employing iSOM-assisted sampling for other reliability estimation methods as well.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102470"},"PeriodicalIF":5.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539338","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":"","authors":"Mitsuyoshi Akiyama","doi":"10.1016/j.strusafe.2024.102471","DOIUrl":"https://doi.org/10.1016/j.strusafe.2024.102471","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"109 ","pages":"Article 102471"},"PeriodicalIF":5.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548405","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":"System risk modelling and decision-making – Reflections and common pitfalls","authors":"Niels Peter Høj ,&nbsp;Inger Birgitte Kroon ,&nbsp;Jannie Sønderkær Nielsen ,&nbsp;Matthias Schubert","doi":"10.1016/j.strusafe.2024.102469","DOIUrl":"10.1016/j.strusafe.2024.102469","url":null,"abstract":"<div><div>Since its foundation, the Joint Committee on Structural Safety (JCSS) has been engaged in the discussion of methods for determining the reliability of components, calibration of standards, as well as risk modelling of systems. In publications, it is regularly explained which methods have which advantages. In the literature, the drawbacks and pitfalls that challenge rational decisions and help to develop and find more appropriate methods for practice are often not documented.</div><div>Such problems can lead to decisions, which are not rational from a decision-theoretic point of view, some of which are worse than a random decision. Especially events, with a very small probability of occurrence hardly give any feedback possibilities from reality and evidence-based analysis of decisions is not possible. Careful selection of methods and knowledge/information of the assumptions is crucial to rational decisions.</div><div>This paper will discuss some of the identified pitfalls based on the discussions in the JCSS. It will span from aspects in the uncertainty quantification, uncertainty propagation, consequence assessment as well as approaches that are found and used in practice for decision-making (e.g. probability interpretations, risk aversion, risk matrices and FN diagrams). This paper can be seen as a documentation of outtakes from the discussions which led to the joint understanding and approach of the JCSS. The paper does not claim to be complete concerning all the possible pitfalls in risk assessments and system identification. But it does provide important reflections and indicates where the eyes must be kept open. Further, the paper points to a way of rational decision-making accounting for the uncertainties in information.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102469"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140403415","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}