Pub Date : 2023-04-03DOI: 10.1080/17499518.2023.2217034
Jia-Lie Ching
Georisk launched the “Spotlight” series in 2013. The purpose of this series is to invite distinguished scholars and practitioners to review an important topic, to highlight research gaps and to suggest fruitful research directions. Volume 17, Issue 2 (2023) of Georisk is pleased to present a Spotlight Article on “Flood hazards in urban environment” by five prominent researchers in the field, Liang Gao, Limin Zhang, Yang Hong, Hongxin Chen and Shijin Feng. Prof. Gao is an assistant professor at the State Key Laboratory of Internet of Things for Smart City and Faculty of Science and Technology, University of Macau, Macao, China. Her research focuses on developing numerical methods for simulating water-related hazards and integrating remote sensing techniques with numerical models. Her research has been supported by several funding agencies. She also serves on the Editorial Board of Georisk. Prof. Zhang is Head and Chair Professor in the Department of Civil and Environmental Engineering of the Hong Kong University of Science and Technology. He is also Director of Geotechnical Centrifuge Facility and Associate Director of GREAT Smart Cities Institute. Prof. Zhang is Editor-in-Chief of Georisk. He received the 2023 Ralph B. Peck Award from the American Society of Civil Engineers (ASCE). Prof. Hong is Chair Professor with NOAA/National Weather Centre and the University of Oklahoma. His research interests include hydrological modelling, water resources management, radar and satellite remote sensing retrieval/validation/application, and data assimilation systems for improved hazard prediction under a changing climate. Dr Hong has published more than 350 refereed articles, books and book chapters, which have been cited for more than 23,000 times. Prof. Chen is an associate professor in the Department of Geotechnical Engineering of Tongji University. His research interests include geoenvironmental engineering and numerical modelling of natural hazards. He is Associate Editor of Natural Hazards Review-ASCE and Journal of Intelligent Construction. Prof. Feng is Chair Professor of the Department of Geotechnical Engineering of Tongji University. His research interests include geoenvironmental engineering and soil dynamics. He was Young Chief Scientist of the “973 Program” and the recipient of the National Science Fund for Distinguished Young Scholars and first-class prize of Shanghai Science and Technology Progress Award. Effective urban flood risk management requires accurate estimation of flood inundation extent and fast information exchange. The urban environment is featured by anthropogenic changes, impervious land cover, artificial surface and underground drainage systems, and densely populated building clusters. Urban flood hazard analysis is therefore more challenging. This Spotlight Article presents a critical review of the basic theory, major urban environment factors, modelling approaches and uncertainties related to the evaluation of flood hazards i
乔治克于2013年推出了“聚焦”系列节目。该系列的目的是邀请杰出的学者和实践者回顾一个重要的主题，突出研究空白，并提出富有成效的研究方向。《Georisk》第17卷第2期(2023年)将为您呈现由高亮、张利民、洪杨、陈鸿欣和冯世进五位杰出研究人员撰写的“城市环境中的洪水灾害”专题文章。高教授现为澳门大学智慧城市物联网国家重点实验室及科技学院助理教授。她的研究重点是开发模拟水相关灾害的数值方法，并将遥感技术与数值模型相结合。她的研究得到了几家资助机构的支持。她还在Georisk的编辑委员会任职。张教授现任香港科技大学土木及环境工程学系系主任及讲座教授。他也是岩土工程离心机设施主任和GREAT智能城市研究所副主任。张教授是Georisk的主编。他获得了美国土木工程师协会(ASCE)颁发的2023年Ralph B. Peck奖。洪教授是美国国家海洋和大气管理局/国家气象中心和俄克拉荷马大学的讲座教授。他的研究兴趣包括水文模型，水资源管理，雷达和卫星遥感检索/验证/应用，以及用于改善气候变化下灾害预测的数据同化系统。洪博士发表了350多篇论文、书籍和书籍章节，被引用次数超过23000次。陈教授为同济大学岩土工程系副教授。主要研究方向为地球环境工程和自然灾害数值模拟。他是《自然灾害评论》和《智能建筑杂志》的副主编。冯教授现任同济大学岩土工程系讲座教授。主要研究方向为地球环境工程和土壤动力学。“973计划”青年首席科学家、国家杰出青年科学基金获得者、上海市科学技术进步一等奖获得者。有效的城市洪水风险管理需要准确的洪水淹没程度估计和快速的信息交换。城市环境的特点是人为变化，不透水的土地覆盖，人工地表和地下排水系统，人口密集的建筑群。因此，城市洪涝灾害分析更具挑战性。本文对城市环境洪水灾害评价的基本理论、主要城市环境因子、建模方法和不确定性进行了综述。它首先从多学科的角度回顾了城市洪水的知识状况，然后介绍了基于物理的建模方法和城市环境因素。城市暴雨水流的数值模拟已成为一种标准做法。结合地表径流和管道流量的建模能力对于预测城市洪水灾害和呈现时空分析结果至关重要。城市环境因素，如建筑物分布、不透水地表表现、地表流和排水网络流的整合，必须在危害评估中得到适当的体现。水力建模过程中的不确定性主要来源于输入数据、模型参数、模型结构和验证过程。当一个水力模型被校准并应用于预测时，不确定性也来自校准参数的可变性、验证算法、假设和分析模型的局限性。在理解过程相关指标方面有希望的进展依赖于改进的测量技术和物理模型的发展。数据丰富的环境鼓励开发新模型和新见解。作者认为，在城市环境中评价暴雨引发的洪涝灾害是一个多方面的问题
{"title":"Spotlight article “Flood hazards in urban environment”","authors":"Jia-Lie Ching","doi":"10.1080/17499518.2023.2217034","DOIUrl":"https://doi.org/10.1080/17499518.2023.2217034","url":null,"abstract":"Georisk launched the “Spotlight” series in 2013. The purpose of this series is to invite distinguished scholars and practitioners to review an important topic, to highlight research gaps and to suggest fruitful research directions. Volume 17, Issue 2 (2023) of Georisk is pleased to present a Spotlight Article on “Flood hazards in urban environment” by five prominent researchers in the field, Liang Gao, Limin Zhang, Yang Hong, Hongxin Chen and Shijin Feng. Prof. Gao is an assistant professor at the State Key Laboratory of Internet of Things for Smart City and Faculty of Science and Technology, University of Macau, Macao, China. Her research focuses on developing numerical methods for simulating water-related hazards and integrating remote sensing techniques with numerical models. Her research has been supported by several funding agencies. She also serves on the Editorial Board of Georisk. Prof. Zhang is Head and Chair Professor in the Department of Civil and Environmental Engineering of the Hong Kong University of Science and Technology. He is also Director of Geotechnical Centrifuge Facility and Associate Director of GREAT Smart Cities Institute. Prof. Zhang is Editor-in-Chief of Georisk. He received the 2023 Ralph B. Peck Award from the American Society of Civil Engineers (ASCE). Prof. Hong is Chair Professor with NOAA/National Weather Centre and the University of Oklahoma. His research interests include hydrological modelling, water resources management, radar and satellite remote sensing retrieval/validation/application, and data assimilation systems for improved hazard prediction under a changing climate. Dr Hong has published more than 350 refereed articles, books and book chapters, which have been cited for more than 23,000 times. Prof. Chen is an associate professor in the Department of Geotechnical Engineering of Tongji University. His research interests include geoenvironmental engineering and numerical modelling of natural hazards. He is Associate Editor of Natural Hazards Review-ASCE and Journal of Intelligent Construction. Prof. Feng is Chair Professor of the Department of Geotechnical Engineering of Tongji University. His research interests include geoenvironmental engineering and soil dynamics. He was Young Chief Scientist of the “973 Program” and the recipient of the National Science Fund for Distinguished Young Scholars and first-class prize of Shanghai Science and Technology Progress Award. Effective urban flood risk management requires accurate estimation of flood inundation extent and fast information exchange. The urban environment is featured by anthropogenic changes, impervious land cover, artificial surface and underground drainage systems, and densely populated building clusters. Urban flood hazard analysis is therefore more challenging. This Spotlight Article presents a critical review of the basic theory, major urban environment factors, modelling approaches and uncertainties related to the evaluation of flood hazards i","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45355195","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}
Pub Date : 2023-04-03DOI: 10.3390/geohazards4020007
Y. Sakuno, Sakito Hirao, N. Taniguchi
The final goal of this paper is to contribute to the difficult task of understanding and forecasting submarine volcanic eruption activity by proposing a method to quantify discolored water. To achieve this purpose, we quantitatively analyzed the discolored seawater seen before and after the eruption of the marine environment around the Indonesian submarine volcano “Anak Krakatau”, which erupted at the end of December 2018, from the viewpoint of the “dominant wavelength”. The atmospherically corrected COM-C SGLI data for 17 periods from the eruption from October 2018 to March 2019 were used. As a result, the following three main items were found. First, the average ± standard deviation of the entire dominant wavelength was 497 nm ± 2 nm before the eruption and 515 nm ± 35 nm after the eruption. Second, the discolored water area around the island derived from SGLI was detected from the contour line with dominant wavelengths of 500 nm and 560 nm. Third, the size of a dominant wavelength of 500 nm or more in the discolored water areas changed in a complicated manner within the range of almost 0 to 35 km2. The area of the dominant wavelength of 500 nm or more slightly increased just before the eruption. Finally, it was proven that the “dominant wavelength” from the SGLI proposed in this paper can be a very effective tool in understanding or predicting submarine volcanic activity.
{"title":"Quantitatively Mapping Discolored Seawater around Submarine Volcanoes Using Satellite GCOM-C SGLI Data: A Case Study of the Krakatau Eruption in Indonesia in December 2018","authors":"Y. Sakuno, Sakito Hirao, N. Taniguchi","doi":"10.3390/geohazards4020007","DOIUrl":"https://doi.org/10.3390/geohazards4020007","url":null,"abstract":"The final goal of this paper is to contribute to the difficult task of understanding and forecasting submarine volcanic eruption activity by proposing a method to quantify discolored water. To achieve this purpose, we quantitatively analyzed the discolored seawater seen before and after the eruption of the marine environment around the Indonesian submarine volcano “Anak Krakatau”, which erupted at the end of December 2018, from the viewpoint of the “dominant wavelength”. The atmospherically corrected COM-C SGLI data for 17 periods from the eruption from October 2018 to March 2019 were used. As a result, the following three main items were found. First, the average ± standard deviation of the entire dominant wavelength was 497 nm ± 2 nm before the eruption and 515 nm ± 35 nm after the eruption. Second, the discolored water area around the island derived from SGLI was detected from the contour line with dominant wavelengths of 500 nm and 560 nm. Third, the size of a dominant wavelength of 500 nm or more in the discolored water areas changed in a complicated manner within the range of almost 0 to 35 km2. The area of the dominant wavelength of 500 nm or more slightly increased just before the eruption. Finally, it was proven that the “dominant wavelength” from the SGLI proposed in this paper can be a very effective tool in understanding or predicting submarine volcanic activity.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83468516","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}
Pub Date : 2023-03-16DOI: 10.3390/geohazards4010006
P. Krassakis, A. Karavias, P. Nomikou, K. Karantzalos, N. Koukouzas, Ioannis Athinelis, S. Kazana, I. Parcharidis
Coastal environments are highly recognized for their spectacular morphological features and economic activities, such as agriculture, maritime traffic, fishing, and tourism. In the context of climate change and the evolution of physical processes, the occurrence of intense natural phenomena adjacent to populated coastal areas may result in natural hazards, causing human and/or structural losses. As an outcome, scientific interest in researching and assessing multi-hazard susceptibility techniques has increased rapidly in an effort to better understand spatial patterns that are threatening coastal exposed elements, with or without temporal coincidence. The islands of Milos and Thira (Santorini Island) in Greece are prone to natural hazards due to their unique volcano-tectonic setting, the high number of tourist visits annually, and the unplanned expansion of urban fabric within the boundaries of the low-lying coastal zone. The main goal of this research is to analyze the onshore coastal terrain’s susceptibility to natural hazards, identifying regions that are vulnerable to soil erosion, torrential flooding, landslides and tsunamis. Therefore, the objective of this work is the development of a multi-hazard approach to the South Aegean Volcanic Arc (SAVA) islands, integrating them into a superimposed susceptibility map utilizing Multi-Criteria Decision-Making (MCDM) analysis. The illustrated geospatial workflow introduces a promising multi-hazard tool that can be implemented in low-lying coastal regions globally, regardless of their morphometric and manmade characteristics. Consequently, findings indicated that more than 30% of built-up areas, 20% of the transportation network, and 50% of seaports are within the high and very high susceptible zones, in terms of the Extended Low Elevation Coastal Zone (ELECZ). Coastal managers and decision-makers must develop a strategic plan in order to minimize potential economic and natural losses, private property damage, and tourism infrastructure degradation from potential inundation and erosion occurrences, which are likely to increase in the foreseeable future.
{"title":"Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process in Coastal Regions of South Aegean Volcanic Arc Islands","authors":"P. Krassakis, A. Karavias, P. Nomikou, K. Karantzalos, N. Koukouzas, Ioannis Athinelis, S. Kazana, I. Parcharidis","doi":"10.3390/geohazards4010006","DOIUrl":"https://doi.org/10.3390/geohazards4010006","url":null,"abstract":"Coastal environments are highly recognized for their spectacular morphological features and economic activities, such as agriculture, maritime traffic, fishing, and tourism. In the context of climate change and the evolution of physical processes, the occurrence of intense natural phenomena adjacent to populated coastal areas may result in natural hazards, causing human and/or structural losses. As an outcome, scientific interest in researching and assessing multi-hazard susceptibility techniques has increased rapidly in an effort to better understand spatial patterns that are threatening coastal exposed elements, with or without temporal coincidence. The islands of Milos and Thira (Santorini Island) in Greece are prone to natural hazards due to their unique volcano-tectonic setting, the high number of tourist visits annually, and the unplanned expansion of urban fabric within the boundaries of the low-lying coastal zone. The main goal of this research is to analyze the onshore coastal terrain’s susceptibility to natural hazards, identifying regions that are vulnerable to soil erosion, torrential flooding, landslides and tsunamis. Therefore, the objective of this work is the development of a multi-hazard approach to the South Aegean Volcanic Arc (SAVA) islands, integrating them into a superimposed susceptibility map utilizing Multi-Criteria Decision-Making (MCDM) analysis. The illustrated geospatial workflow introduces a promising multi-hazard tool that can be implemented in low-lying coastal regions globally, regardless of their morphometric and manmade characteristics. Consequently, findings indicated that more than 30% of built-up areas, 20% of the transportation network, and 50% of seaports are within the high and very high susceptible zones, in terms of the Extended Low Elevation Coastal Zone (ELECZ). Coastal managers and decision-makers must develop a strategic plan in order to minimize potential economic and natural losses, private property damage, and tourism infrastructure degradation from potential inundation and erosion occurrences, which are likely to increase in the foreseeable future.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73257976","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}
Pub Date : 2023-03-16DOI: 10.1080/17499518.2023.2188464
Peiping Li, Yu Wang
ABSTRACT Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement. However, precipitation monitoring data are collected through a limited number of precipitation stations in practice, and they are often sparse and discontinuous, particularly in spatial domain. Furthermore, regional precipitation data exhibits characteristics of seasonality, periodicity and highly non-stationarity on a long-time scale. Therefore, it is challenging to obtain a spatio-temporal variation of precipitation with high spatial resolution from monitoring data measured at a limited number of precipitation stations. To address these challenges, this study develops a non-parametric spatio-temporal Bayesian compressive sensing (ST-BCS) method for interpolation of spatio-temporally varying, but sparsely measured precipitation data in the spatial domain. The proposed method is able to not only provide precipitation interpolation results with high spatial resolution from a limited number of monitoring stations, but also quantify the associated interpolation uncertainty simultaneously. In addition, ST-BCS is directly applicable to the non-stationary spatio-temporal meteorological data. Furthermore, real precipitation datasets are established to benchmark different spatio-temporal interpolation methods. The benchmarking results show that the proposed ST-BCS method performs well and outperforms the spatial BCS method.
{"title":"Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)","authors":"Peiping Li, Yu Wang","doi":"10.1080/17499518.2023.2188464","DOIUrl":"https://doi.org/10.1080/17499518.2023.2188464","url":null,"abstract":"ABSTRACT Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement. However, precipitation monitoring data are collected through a limited number of precipitation stations in practice, and they are often sparse and discontinuous, particularly in spatial domain. Furthermore, regional precipitation data exhibits characteristics of seasonality, periodicity and highly non-stationarity on a long-time scale. Therefore, it is challenging to obtain a spatio-temporal variation of precipitation with high spatial resolution from monitoring data measured at a limited number of precipitation stations. To address these challenges, this study develops a non-parametric spatio-temporal Bayesian compressive sensing (ST-BCS) method for interpolation of spatio-temporally varying, but sparsely measured precipitation data in the spatial domain. The proposed method is able to not only provide precipitation interpolation results with high spatial resolution from a limited number of monitoring stations, but also quantify the associated interpolation uncertainty simultaneously. In addition, ST-BCS is directly applicable to the non-stationary spatio-temporal meteorological data. Furthermore, real precipitation datasets are established to benchmark different spatio-temporal interpolation methods. The benchmarking results show that the proposed ST-BCS method performs well and outperforms the spatial BCS method.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46162480","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}
ABSTRACT Landslide hazard assessment is an important component of risk management and land-use planning. This study aims to investigate the application of a physically-based model named after the fast shallow landslide assessment model (FSLAM) to rainfall-triggered landslide hazard assessment. In August 2015, a total of 123 landslides induced by Typhoon Soudelor in Wenzhou City, southeastern China, was taken as an example. Five input raster files (elevation, soil types, vegetation, antecedent rainfall, event rainfall) and two parameter files regarding soil properties and vegetation were determined. Considering the randomness and uncertainty of soil and vegetation parameters on the regional scale, FSLAM model computes the probability of failure (PoF) by using random parameters inputs. Finally, the landslide hazard map was generated for the study area to reflect the landslide risk. The results showed that FSLAM could accurately capture the effect of rainfall on PoF of slopes, and more than 70% of the landslide were identified in very high/high hazard zones. The accuracy of the receiver operating characteristic (ROC) reached 0.720, which was higher than that of the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (0.620). Regarding the computational time, FSLAM had better efficiency, and the consuming time was 1/25 compared with TRIGRS.
{"title":"Hazard assessment for regional typhoon-triggered landslides by using physically-based model – a case study from southeastern China","authors":"Zizheng Guo, Bixia Tian, Jun He, Chong Xu, Taorui Zeng, Yuhang Zhu","doi":"10.1080/17499518.2023.2188465","DOIUrl":"https://doi.org/10.1080/17499518.2023.2188465","url":null,"abstract":"ABSTRACT Landslide hazard assessment is an important component of risk management and land-use planning. This study aims to investigate the application of a physically-based model named after the fast shallow landslide assessment model (FSLAM) to rainfall-triggered landslide hazard assessment. In August 2015, a total of 123 landslides induced by Typhoon Soudelor in Wenzhou City, southeastern China, was taken as an example. Five input raster files (elevation, soil types, vegetation, antecedent rainfall, event rainfall) and two parameter files regarding soil properties and vegetation were determined. Considering the randomness and uncertainty of soil and vegetation parameters on the regional scale, FSLAM model computes the probability of failure (PoF) by using random parameters inputs. Finally, the landslide hazard map was generated for the study area to reflect the landslide risk. The results showed that FSLAM could accurately capture the effect of rainfall on PoF of slopes, and more than 70% of the landslide were identified in very high/high hazard zones. The accuracy of the receiver operating characteristic (ROC) reached 0.720, which was higher than that of the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (0.620). Regarding the computational time, FSLAM had better efficiency, and the consuming time was 1/25 compared with TRIGRS.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41554640","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}
Pub Date : 2023-03-14DOI: 10.1080/17499518.2023.2188467
D. Damasceno, J. Spross, F. Johansson
{"title":"Reliability-based design tool for gas storage in lined rock caverns","authors":"D. Damasceno, J. Spross, F. Johansson","doi":"10.1080/17499518.2023.2188467","DOIUrl":"https://doi.org/10.1080/17499518.2023.2188467","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49409781","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}
Pub Date : 2023-03-13DOI: 10.1080/17499518.2023.2188466
Yuchao Li, Jianping Chen, F. Zhou, Xin Zhou, Zhihai Li, Qing Wang
{"title":"Stochastic kinematic analysis of rock slope failure angle based on multi algorithm optimization, a case study of the proposed bridge project","authors":"Yuchao Li, Jianping Chen, F. Zhou, Xin Zhou, Zhihai Li, Qing Wang","doi":"10.1080/17499518.2023.2188466","DOIUrl":"https://doi.org/10.1080/17499518.2023.2188466","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44629129","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}
Pub Date : 2023-03-10DOI: 10.3390/geohazards4010005
M. Allahbakhshi, A. Shevchenko, A. Belousov, M. Belousova, H. Kämpf, T. Walter
Geyser geothermal fields are scenic volcanic landforms that often contain tens to hundreds of thermal spot vents that erupt boiling water or contain bubbling mud pools. The fields are potentially hazardous sites due to boiling water temperatures and changes in vent locations and eruption dynamics, which are poorly understood. Here we report on the rapid and profound changes that can affect such a geyser field and ultimately lead to a dangerous, unanticipated eruption. We studied the Geyser Valley, Kamchatka Peninsula, which is a field of geysers and other thermal features and boiling pools. Using high-resolution tri-stereo satellite data and unmanned aerial systems (UAS) with optical and thermal infrared cameras in 2018 and 2019, we were able to identify a newly emerging explosion site. Structure-from-motion analysis of data acquired before and after the explosion reveals morphological and thermal details of the new vent. The explosion site produced an aureole zone of more than 150 m3 of explosively redeposited gravel and clay, a slightly elliptical crater with a diameter of 7.5 m and a crater rim 0.30 m high. However, comparison with archives of photogrammetric data suggests that this site was thermally active years earlier and contained a crater that was obscured and covered by landslides and river sediments. The results allow us to develop a conceptual model and highlight the hazard potential of thermal features buried by landslides and clastic deposits. Sudden explosions may occur at similar sites elsewhere, highlighting the need for careful assessment and monitoring of geomorphological and hydrological changes at geyser sites in other regions.
{"title":"Geothermal Explosion at the 2014 Landslide-Covered Area of the Geyser Valley, Kamchatka, Russian Far East","authors":"M. Allahbakhshi, A. Shevchenko, A. Belousov, M. Belousova, H. Kämpf, T. Walter","doi":"10.3390/geohazards4010005","DOIUrl":"https://doi.org/10.3390/geohazards4010005","url":null,"abstract":"Geyser geothermal fields are scenic volcanic landforms that often contain tens to hundreds of thermal spot vents that erupt boiling water or contain bubbling mud pools. The fields are potentially hazardous sites due to boiling water temperatures and changes in vent locations and eruption dynamics, which are poorly understood. Here we report on the rapid and profound changes that can affect such a geyser field and ultimately lead to a dangerous, unanticipated eruption. We studied the Geyser Valley, Kamchatka Peninsula, which is a field of geysers and other thermal features and boiling pools. Using high-resolution tri-stereo satellite data and unmanned aerial systems (UAS) with optical and thermal infrared cameras in 2018 and 2019, we were able to identify a newly emerging explosion site. Structure-from-motion analysis of data acquired before and after the explosion reveals morphological and thermal details of the new vent. The explosion site produced an aureole zone of more than 150 m3 of explosively redeposited gravel and clay, a slightly elliptical crater with a diameter of 7.5 m and a crater rim 0.30 m high. However, comparison with archives of photogrammetric data suggests that this site was thermally active years earlier and contained a crater that was obscured and covered by landslides and river sediments. The results allow us to develop a conceptual model and highlight the hazard potential of thermal features buried by landslides and clastic deposits. Sudden explosions may occur at similar sites elsewhere, highlighting the need for careful assessment and monitoring of geomorphological and hydrological changes at geyser sites in other regions.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81234405","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}
Pub Date : 2023-03-07DOI: 10.1080/17499518.2023.2182890
Bo Du, Tao Zou, Junchen Ye, X. Tan, Ke-Yu Cheng, Wei-zhong Chen
{"title":"Prediction of tunnel mechanical behaviour using multi-task deep learning under the external condition","authors":"Bo Du, Tao Zou, Junchen Ye, X. Tan, Ke-Yu Cheng, Wei-zhong Chen","doi":"10.1080/17499518.2023.2182890","DOIUrl":"https://doi.org/10.1080/17499518.2023.2182890","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42231588","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}
Pub Date : 2023-02-19DOI: 10.1080/17499518.2023.2174264
W. Guo, Xiaohui Tan, Jie Zhang, Xin Lin, Xiaole Dong, Xiaoliang Hou
ABSTRACT Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength.
{"title":"System reliability and sensitivity analysis of lateral loaded pile considering soil’s spatial variability","authors":"W. Guo, Xiaohui Tan, Jie Zhang, Xin Lin, Xiaole Dong, Xiaoliang Hou","doi":"10.1080/17499518.2023.2174264","DOIUrl":"https://doi.org/10.1080/17499518.2023.2174264","url":null,"abstract":"ABSTRACT Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45066794","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}
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