Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards最新文献

{"title":"Collapsing Response of a Nonlinear Shear-Beam Building Model Excited by a Strong-Motion Pulse at Its Base","authors":"H. Abbasgholiha, V. Gičev, M. Trifunac, R. S. Jalali, M. Todorovska","doi":"10.3390/geohazards4010004","DOIUrl":"https://doi.org/10.3390/geohazards4010004","url":null,"abstract":"We present a simple nonlinear model of a shear-beam building that experiences large nonlinear deformations and collapse when excited by large pulses of strong earthquake ground motion. In this paper, we introduce the model and show that its properties can be selected to be consistent with the damage observed in a seven-story hotel in San Fernando Valley of the Los Angeles metropolitan area during the 1994 Northridge earthquake. We also show an example of excitation that leads to the collapse of the model. We illustrate the response only for a sequence of horizontal pulses. We will describe the response of the same model to horizontal, vertical, and rocking motions at its base, as well as for more general excitation by strong earthquake ground motion, in future papers.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75861796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ground Investigations and Detection and Monitoring of Landslides Using SAR Interferometry in Gangtok, Sikkim Himalaya","authors":"R. Bhasin, Gokhan Aslan, J. Dehls","doi":"10.3390/geohazards4010003","DOIUrl":"https://doi.org/10.3390/geohazards4010003","url":null,"abstract":"The Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74529232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgment to the Reviewers of GeoHazards in 2022","authors":"","doi":"10.3390/geohazards4010002","DOIUrl":"https://doi.org/10.3390/geohazards4010002","url":null,"abstract":"High-quality academic publishing is built on rigorous peer review [...]","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89474764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk evaluation for earth-fill dams due to heavy rains by response surface method","authors":"Shi-hua Zheng, S. Nishimura, T. Shuku, T. Shibata, Tsubasa Tateishi","doi":"10.1080/17499518.2023.2164901","DOIUrl":"https://doi.org/10.1080/17499518.2023.2164901","url":null,"abstract":"ABSTRACT This paper discusses a risk evaluation for earth-fill dams due to heavy rains. The detailed method employs a flood analysis and land use data to calculate the costs of the inundation damage in the downstream areas of earth-fill dams. The procedure to calculate the damage costs requires a lot of labour. Since a huge number of earth-fill dams exist in Japan, a straightforward method is needed. The response surface method, one of the surrogate models, is proposed in this study to reduce the calculation effort. The optimum response surface is firstly evaluated by cross validation, and then the accuracy is verified by comparing the damage costs obtained by the response surface method with those obtained by the detailed method for the earth-fill dam sites. To calculate the risks, it is necessary to determine the probability of overflow failure due to heavy rains. The risk of breaching is calculated from the product of the probability of overflow failure and the estimated damage costs. The accuracy of the response surface method is assessed by comparing the risk rankings of the dams, which is the priority in dam renovations, between the detailed and the response surface methods.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49589586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time capsule for landslide risk assessment","authors":"Yu Lei, Jinsong Huang, Yifei Cui, Shui-Hua Jiang, Sheng-nan Wu, J. Ching","doi":"10.1080/17499518.2023.2164899","DOIUrl":"https://doi.org/10.1080/17499518.2023.2164899","url":null,"abstract":"ABSTRACT Landslides, one of the most common mountain hazards, can result in enormous casualties and huge economic losses in mountainous regions. In order to address the landslide hazards effectively, the geological society is required not only to develop in-depth understanding of landslide mechanism but also to quantify its associated risk. In this article, landslide risk assessment is categorised into two types, hard and soft risk assessments, and reviewed separately. The hard approach focuses on the mechanics and numerical simulations of individual landslides, subsequent consequences, and their uncertainty quantifications and probabilistic analyses while the soft approach explores the quantification of disaster risk components such as hazard and vulnerability at different scales of concern. It is hoped that this article can serve as a time capsule to link the preceding and following of landslide risk assessments and shed some light on future studies.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47789134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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-based serviceability limit state design of spread foundations under uplift loading in cohesionless soils","authors":"Jayne M. Han, Kyo-Young Gu, Kyeong-Sun Kim, Kyung-Won Ham, Sung-Ryul Kim","doi":"10.1080/17499518.2023.2164900","DOIUrl":"https://doi.org/10.1080/17499518.2023.2164900","url":null,"abstract":"ABSTRACT The design of foundations is often governed by the serviceability limit state (SLS) requirements of the supported structure, particularly for large spread foundations. This paper aims to develop a reliability-based SLS design method for spread foundations under uplift loading in cohesionless soils. A probabilistic framework was adopted for the empirical characterisation of the compiled load-displacement curves and the quantification of the associated uncertainties. By using the obtained statistics of the curves, reliability analysis was carried out with Monte-Carlo simulations to calibrate the resistance factors within the load and resistance factor design (LRFD) framework. The calibration results showed that the embedment ratio of the foundation and the fitting errors of the empirical model, which were previously unaddressed in the literature, had notable effects on the calibrated SLS resistance factors. The relationship of the SLS with the ultimate limit state was assessed, including the governing limit state at each allowable displacement level, and the probability of ultimate failure of the foundation at the SLS condition. By considering the relationship between the limit states, the procedures for determining the design resistance factor and foundation capacity were proposed.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49616726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-project utilisation of tunnel boring machine (TBM) construction data: a case study using big data from Yin-Song diversion project in China","authors":"Xu Li, Haibo Li, Saizhao Du, Liujie Jing, Pengyu Li","doi":"10.1080/17499518.2023.2184834","DOIUrl":"https://doi.org/10.1080/17499518.2023.2184834","url":null,"abstract":"ABSTRACT The variation in Tunnelling boring machine (TBM) equipment and geological information of tunnels result in substantial differences in real-time TBM tunnelling data. This variation makes it difficult to apply machine learning models trained by historical engineering data on new projects. To overcome this challenge, a novel data conversion approach from a mechanical analysis perspective has been proposed to normalise TBM tunnelling data, such as cutterhead torque and cutterhead thrust, which help to unify data from different projects under the same framework. Furthermore, the effectiveness of this approach has been verified through analogy analysis and machine learning applications. With the application of these conversion relationships, the machine learning model trained on a completed Yin-Song project with big data (12,501 boring cycles) is applied to the on-going Yin-Chao Water Diversion Project in China with limited data (777 boring cycles) and gives reliable predictions for each performance parameter (with R2 for the cutterhead thrust of 0.81 and R2 for the cutterhead torque of 0.70). This approach enhances the usefulness of TBM intelligence for cross-engineering geophysical prospecting in different geological conditions.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43920960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal prediction of landslide displacement using deep learning approaches based on monitored time-series displacement data: a case in the Huanglianshu landslide","authors":"N. Xi, M. Zang, Ruoshen Lin, Yingjie Sun, Gang Mei","doi":"10.1080/17499518.2023.2172186","DOIUrl":"https://doi.org/10.1080/17499518.2023.2172186","url":null,"abstract":"ABSTRACT The use of deep learning approaches to predict landslide displacement based on monitored time-series data is an effective method for the early-warning of landslides. Currently, most prediction models focus on the temporal correlation of displacements from a single monitoring point, ignoring the spatial influence of other monitoring points. To fully consider the spatiotemporal features of the displacement data, this paper develops three deep learning models based on graph convolution networks to spatiotemporally predict the landslide displacements of the Huanglianshu landslide. Specifically, we first establish a fully connected graph to represent the spatial relationships of all the deployed monitoring points. Second, we develop a temporal graph convolutional network-long short term memory (TGCN-LSTM) model and an Attention-TGCN model based on the temporal graph convolutional network-gate recurrent unit (TGCN-GRU) deep learning model and employ the three models to spatiotemporally predict displacements of the Huanglianshu landslide. The proposed spatiotemporal prediction models accurately predict the displacements at seven monitoring points, with a maximum R 2 of 0.85 at the individual monitoring points. The comparative results show that the proposed Attention-TGCN model achieves the highest spatiotemporal prediction accuracy, and the accuracy of the Attention-TGCN model can further improve after considering the movement of the monitoring points.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44313965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on “Machine learning and AI in geotechnics”","authors":"K. Phoon, L. M. Zhang, Z. Cao","doi":"10.1080/17499518.2023.2185938","DOIUrl":"https://doi.org/10.1080/17499518.2023.2185938","url":null,"abstract":"The potential for machine learning and artificial intelligence to shape geotechnical engineering practice (and possibly theory) is immense. However, the agenda for machine learning in geotechnics should not be focused on applying or developing algorithms alone. The geotechnical context that gives rise to the data is important. The context can be related to statistics, physics, or experience. Statistics refer to the attributes of geotechnical data that depart significantly from the assumptions in classical statistics (large sample size, spatial/temporal/parametric independence, homogeneity, normality, etc.). Phoon, Ching, and Shuku (2022a) argued that geotechnical site data are “ugly”, because they are spatially varying, sparse, site-specific (or unique to some extent), and incomplete in the sense that a multivariate database is full of empty entries denoting lack of some measurements at certain locations/depths. The incompleteness attribute arises from an intent to maximize information on cross correlations between different soil parameters and geotechnical/geologic spatial correlations across a given site while minimizing the site investigation budget. At this point, this value of information optimization is an art rather than a science. The scientific challenge to draw useful insights from MUSIC-3X (Multivariate, Uncertain and Unique, Sparse, Incomplete, and potentially Corrupted with “3X” denoting 3D spatial variability) data was thought to be intractable until recently (Phoon, Ching, and Shuku 2022a). These “ugly” data attributes are the norm in a site investigation report. In rock engineering, data can be categorical rather than numerical. Phoon (2023) emphasized that “decision making in every discipline is supported by its own data with unique attributes and a tradition of successful practice (investigation, design, construction, testing, monitoring, and risk management methodology) that evolved to make the best use of these data and prevailing technologies”. Physics refers to a body of rational knowledge that associates a “number” to “meaning”. Decisions supported by physics-informed results are “explainable” and “interpretable”. The finite element method is the most prevalent embodiment of physics in geotechnical engineering. Using finite element analysis, an engineer understands cause and effect (interpretability) and knows which input parameters affect the outputs (explainability). An engineer distinguishes between material and state parameters, between effective and total stress parameters, and between input and output parameters from a physical or numerical model. These distinctions exist when one approaches data from the lens of physics. Experience refers to a body of empirical knowledge accrued from deliberate practice. It is restricted by the range of projects encountered by an engineer over his/ her working life and it cannot be shared with other engineers efficiently. In contrast to statistics and physics, it is mainly subjective","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45961885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 real-time intelligent classification model using machine learning for tunnel surrounding rock and its application","authors":"Junjie Ma, Tianbin Li, Gang Yang, Kunkun Dai, Chun-chi Ma, Hao Tang, Gangwei Wang, Jianfeng Wang, Bo Xiao, Lu-bo Meng","doi":"10.1080/17499518.2023.2182891","DOIUrl":"https://doi.org/10.1080/17499518.2023.2182891","url":null,"abstract":"ABSTRACT Real-time and accurate prediction of surrounding rock grade is crucial for tunnel dynamic construction and design. However, the internationally accepted semi-quantitative methods (e.g. rock mass rating (RMR), Q, and basic quality (BQ)) cannot provide fast and accurate classification in construction. This study proposed an intelligent surrounding rock classification method and a tunnel information management system, which can predict the surrounding rock grade in real-time and accurately. A database is collected with 286 cases in China, including seven geological parameters and surrounding rock grades. Based on different training parameters, 12 classification models are established using VGGNet, ResNet, and support vector machine (SVM) algorithms. The accuracy of the SVM classifier is 93.02%, which performs better than the VGGNet and ResNet classifiers. Moreover, precision, recall, F-measure, receiver operating characteristic (ROC), and 20-case verification show that the SVM classification model has greater robustness in learning and generalising for small and imbalanced samples. Additionally, a tunnel information management system is developed with cloud technology, which can accurately predict the surrounding rock grade within 10 s. Overall, the achievements of this study can provide valuable references for real-time rock mass classification in traffic tunnels and underground powerhouses.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49088831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}