Pub Date : 2023-11-10DOI: 10.3390/geohazards4040026
Jean-Paul Deroin
The Mont-Saint-Michel is known worldwide for its unique combination of the natural site and the Medieval abbey at the top of the rocky islet. But the Mont is also located within an estuarine complex, which is considerably silting up. For two decades, large-scale works were planned to prevent the Mont from being surrounded by the expanding salt meadows. The construction of a new dam over the Couesnon River, the digging of two new channels, and the destruction of the causeway were the main operations carried out between 2007 and 2015. The remote sensing approach is fully suitable for evaluating the real impact of the engineering project, particularly the expected large-scale hydrosedimentary effects of reestablishing the maritime landscape around the Mont. The migration of the different channels and the erosion-progradation balance of the vegetation through space and time are the main features to study. Between 2007 and 2023, the erosion of the salt meadows was significant to the south-west of the Mont but more limited to the south-east. During the same period, the sedimentation considerably increased to the north-east of the Bay, which seems to be facing the same silting-up problem. At this stage, the remote-sensing survey indicates mixed results for the engineering project.
{"title":"Evaluating the Impact of Engineering Works in Megatidal Areas Using Satellite Images—Case of the Mont-Saint-Michel Bay, France","authors":"Jean-Paul Deroin","doi":"10.3390/geohazards4040026","DOIUrl":"https://doi.org/10.3390/geohazards4040026","url":null,"abstract":"The Mont-Saint-Michel is known worldwide for its unique combination of the natural site and the Medieval abbey at the top of the rocky islet. But the Mont is also located within an estuarine complex, which is considerably silting up. For two decades, large-scale works were planned to prevent the Mont from being surrounded by the expanding salt meadows. The construction of a new dam over the Couesnon River, the digging of two new channels, and the destruction of the causeway were the main operations carried out between 2007 and 2015. The remote sensing approach is fully suitable for evaluating the real impact of the engineering project, particularly the expected large-scale hydrosedimentary effects of reestablishing the maritime landscape around the Mont. The migration of the different channels and the erosion-progradation balance of the vegetation through space and time are the main features to study. Between 2007 and 2023, the erosion of the salt meadows was significant to the south-west of the Mont but more limited to the south-east. During the same period, the sedimentation considerably increased to the north-east of the Bay, which seems to be facing the same silting-up problem. At this stage, the remote-sensing survey indicates mixed results for the engineering project.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186513","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 : 2023-11-06DOI: 10.1080/17499518.2023.2278136
Yu Wang, Hua-Ming Tian
ABSTRACTGeotechnical engineering is experiencing a paradigm shift towards digital transformation and intelligence, driven by Industry 4.0 and emerging digital technologies, such as machine learning. However, development and application of machine learning are relatively slow in geotechnical practice, because extensive training databases are a key to the success of machine learning, but geotechnical data are often small and ugly, leading to the difficulty in developing a suitable training database required for machine learning. In addition, convincing examples from real projects are rare that demonstrate the immediate added value of machine learning to geotechnical practices. To facilitate digital transformation and machine learning in geotechnical engineering, this study proposes to develop a project-specific training database that leverages on digital transformation of geotechnical workflow and reflects both project-specific data collected from various stages of the geotechnical workflow and domain knowledge in geotechnical practices, such as soil mechanics, numerical analysis principles, and prior engineering experience and judgment. A real ground improvement project is presented to illustrate the proposed method and demonstrate the added value of digital transformation and machine learning in geotechnical practices.KEYWORDS: Machine learningdata-centric geotechnicsdigital transformationdigital intelligencereal project example AcknowledgementsThe work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. CityU 11203322), a grant from the Innovation and Technology Commission of Hong Kong Special Administrative region (Project no: MHP/099/21), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering","authors":"Yu Wang, Hua-Ming Tian","doi":"10.1080/17499518.2023.2278136","DOIUrl":"https://doi.org/10.1080/17499518.2023.2278136","url":null,"abstract":"ABSTRACTGeotechnical engineering is experiencing a paradigm shift towards digital transformation and intelligence, driven by Industry 4.0 and emerging digital technologies, such as machine learning. However, development and application of machine learning are relatively slow in geotechnical practice, because extensive training databases are a key to the success of machine learning, but geotechnical data are often small and ugly, leading to the difficulty in developing a suitable training database required for machine learning. In addition, convincing examples from real projects are rare that demonstrate the immediate added value of machine learning to geotechnical practices. To facilitate digital transformation and machine learning in geotechnical engineering, this study proposes to develop a project-specific training database that leverages on digital transformation of geotechnical workflow and reflects both project-specific data collected from various stages of the geotechnical workflow and domain knowledge in geotechnical practices, such as soil mechanics, numerical analysis principles, and prior engineering experience and judgment. A real ground improvement project is presented to illustrate the proposed method and demonstrate the added value of digital transformation and machine learning in geotechnical practices.KEYWORDS: Machine learningdata-centric geotechnicsdigital transformationdigital intelligencereal project example AcknowledgementsThe work described in this paper was supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region (Project no. CityU 11203322), a grant from the Innovation and Technology Commission of Hong Kong Special Administrative region (Project no: MHP/099/21), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135681271","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 : 2023-11-06DOI: 10.3390/geohazards4040025
Marco Tedesco, Jacek Radzikowski
Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R2 of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1).
提高我们监测洪水事件的技能对于保护人口和基础设施以及规划缓解和适应战略至关重要。尽管最近取得了进展，但由于固有的模型限制和遥感数据，水文模型和遥感工具并不总是有助于以所需的空间和时间分辨率绘制洪水图。在这方面，由网络摄像机收集的图像可以用来估计洪水期间的水位，或者在一个场景中有无水。在这里，我们报告了一种算法的评估结果，该算法使用网络摄像头图像来估计水位，并在洪水事件中检测水的存在。该算法的核心是基于深度卷积神经网络(d - cnn)和图像分割的结合。我们以两种方式评估了算法的输出:首先，我们将算法获得的水位时间序列估计值与配置的潮汐计测量的结果进行了比较;其次，我们对算法进行了定性评估，以检测在不同照明和天气条件下、低空间或光谱分辨率下从网络上获得的图像中是否存在洪水。在这里考虑的两种情况下，测量和相机估计的水位之间的比较表明，决定系数R2为0.84-0.87，最大绝对偏差为2.44-3.04 cm，斜率范围为1.089至1.103。我们对测量水位和相机估计水位的直方图的分析表明，两种测量水位的平均差异分别为- 1.18厘米和5.35厘米，标准差范围为4.94至12.03厘米。我们对该算法从网络上获取的图像以及包含洪水事件前后区域场景的图像中检测水的性能进行了分析，结果表明，该算法的准确率超过了~90%，其中用于评估分割分析输出的Intersection over Union (IoU)和边界F1分数分别超过了~80% (IoU)和70% (BF1)。
{"title":"Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates","authors":"Marco Tedesco, Jacek Radzikowski","doi":"10.3390/geohazards4040025","DOIUrl":"https://doi.org/10.3390/geohazards4040025","url":null,"abstract":"Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R2 of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1).","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590149","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 : 2023-11-01DOI: 10.3390/geohazards4040024
Arjun Kohli, Yunan Li, Tae Wook Kim, Anthony R. Kovscek
California’s Central Valley offers vast opportunities for CO2 storage in deep saline aquifers. We conducted an induced seismicity hazard assessment for a potential injection site in the southern San Joaquin Basin for 18 years of injection at 0.68 MtCO2/yr and 100 years of monitoring. We mapped stress, faults, and seismicity in a 30 km radius around the site to build a geomechanical model and resolve the stresses on major faults. From a 3D hydromechanical simulation of the CO2 plume, we calculated the change in pressure over time on these faults and determined the conditions for safe injection. Lacking any subsurface imaging, we also conducted a probabilistic fault slip analysis using numerous random distributions of faults and a range of geomechanical parameters. Our results show that the change in probability of fault slip can be minimized by controlling the size, migration, and magnitude of the pressure plume. We also constructed a seismic catalog for the last 20 years around the site and characterized the natural patterns of seismicity. We use these results to establish criteria for evaluating potential-induced events during the storage period and to develop a traffic light response system. This study represents a first-order procedure to evaluate the seismic hazards presented by CO2 storage and incorporate uncertainties in hydrological and geomechanical parameters.
{"title":"Induced Seismicity Hazard Assessment for a Potential CO2 Storage Site in the Southern San Joaquin Basin, CA","authors":"Arjun Kohli, Yunan Li, Tae Wook Kim, Anthony R. Kovscek","doi":"10.3390/geohazards4040024","DOIUrl":"https://doi.org/10.3390/geohazards4040024","url":null,"abstract":"California’s Central Valley offers vast opportunities for CO2 storage in deep saline aquifers. We conducted an induced seismicity hazard assessment for a potential injection site in the southern San Joaquin Basin for 18 years of injection at 0.68 MtCO2/yr and 100 years of monitoring. We mapped stress, faults, and seismicity in a 30 km radius around the site to build a geomechanical model and resolve the stresses on major faults. From a 3D hydromechanical simulation of the CO2 plume, we calculated the change in pressure over time on these faults and determined the conditions for safe injection. Lacking any subsurface imaging, we also conducted a probabilistic fault slip analysis using numerous random distributions of faults and a range of geomechanical parameters. Our results show that the change in probability of fault slip can be minimized by controlling the size, migration, and magnitude of the pressure plume. We also constructed a seismic catalog for the last 20 years around the site and characterized the natural patterns of seismicity. We use these results to establish criteria for evaluating potential-induced events during the storage period and to develop a traffic light response system. This study represents a first-order procedure to evaluate the seismic hazards presented by CO2 storage and incorporate uncertainties in hydrological and geomechanical parameters.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270935","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 : 2023-10-26DOI: 10.1080/17499518.2023.2272986
Jiazeng Cao, Tao Wang, Yingying Huang, Bin Zhu, Ruilin Li, Guoqing Zhou
ABSTRACTHow to use limited investigation data to analyse the failure of roof engineering is a challenging problem. In this paper, a novel risk analysis method for the mechanical structure of a roof is proposed. Firstly, the Copula theory is presented and the construction method of multidimensional Gaussian Copula parameters is given. Secondly, a Copula method of the mechanical characteristics and instability risk of roof structure with E, ν, c and φ as uncertain variables is proposed. Thirdly, based on the investigation data of 192 roof groups in China, the influence of Copula mechanical parameters on the failure probability of the roof structure is analysed and discussed. This new evaluation methodology can use various Copula functions to simulate the positive and negative correlation structures, which provides an effective way to clarify the mechanical characteristics and instability risk of roof structure using limited investigation data. The results show that the mechanical failure of the roof structure is mainly at the bottom. As the correlation of mechanical parameters increases, the failure probability of the mechanical structure decreases significantly. In the simulation of positive and negative correlation parameters, Gaussian Copula and No.16 Copula, respectively, make the roof have the smallest failure probability.KEYWORDS: Mechanical behaviourinstability riskroof structurereliabilityfailure probability Additional informationFundingThis research was supported by the National Natural Science Foundation of China [grant numbers 42371133 and 42372329], the Open Fund of State Key Laboratory of Coal Mining and Clean Utilization (China Coal Research Institute) [grant number 2021-CMCU-KF019], the Opening Fund of Technology Innovation Center for Mine Geological Environment Restoration in the Alpine and Arid Regions [grant number HHGCKK2205], and the Opening Fund of Key Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources (Fujian Key Laboratory of Geohazard Prevention) [grant number FJKLGH2023K003].
{"title":"Novel evaluation methodology for mechanical behaviour and instability risk of roof structure using limited investigation data","authors":"Jiazeng Cao, Tao Wang, Yingying Huang, Bin Zhu, Ruilin Li, Guoqing Zhou","doi":"10.1080/17499518.2023.2272986","DOIUrl":"https://doi.org/10.1080/17499518.2023.2272986","url":null,"abstract":"ABSTRACTHow to use limited investigation data to analyse the failure of roof engineering is a challenging problem. In this paper, a novel risk analysis method for the mechanical structure of a roof is proposed. Firstly, the Copula theory is presented and the construction method of multidimensional Gaussian Copula parameters is given. Secondly, a Copula method of the mechanical characteristics and instability risk of roof structure with E, ν, c and φ as uncertain variables is proposed. Thirdly, based on the investigation data of 192 roof groups in China, the influence of Copula mechanical parameters on the failure probability of the roof structure is analysed and discussed. This new evaluation methodology can use various Copula functions to simulate the positive and negative correlation structures, which provides an effective way to clarify the mechanical characteristics and instability risk of roof structure using limited investigation data. The results show that the mechanical failure of the roof structure is mainly at the bottom. As the correlation of mechanical parameters increases, the failure probability of the mechanical structure decreases significantly. In the simulation of positive and negative correlation parameters, Gaussian Copula and No.16 Copula, respectively, make the roof have the smallest failure probability.KEYWORDS: Mechanical behaviourinstability riskroof structurereliabilityfailure probability Additional informationFundingThis research was supported by the National Natural Science Foundation of China [grant numbers 42371133 and 42372329], the Open Fund of State Key Laboratory of Coal Mining and Clean Utilization (China Coal Research Institute) [grant number 2021-CMCU-KF019], the Opening Fund of Technology Innovation Center for Mine Geological Environment Restoration in the Alpine and Arid Regions [grant number HHGCKK2205], and the Opening Fund of Key Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources (Fujian Key Laboratory of Geohazard Prevention) [grant number FJKLGH2023K003].","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381926","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 : 2023-10-22DOI: 10.3390/geohazards4040023
Philippe Rosset, Xuejiao Long, Luc Chouinard
Greater Montreal is situated in a region with moderate seismic activity and rests on soft ground deposits from the ancient Champlain Sea, as well as more recent alluvial deposits from the Saint Lawrence River. These deposits have the potential to amplify seismic waves, as demonstrated by past strong, and recent weak, earthquakes. Studies based on the 2015 National Seismic Hazard Model (SHM5) had estimated losses to residential buildings at 2% of their value for an event with a return period of 2475 years. In 2020, the seismic hazard model was updated (SHM6), resulting in more severe hazards for eastern Canada. This paper aims to quantify the impact of these changes on losses to residential buildings in Greater Montreal. Our exposure database includes population and buildings at the scale of dissemination areas (500–1000 inhabitants). Buildings are classified by occupancy and construction type and grouped into three building code levels based on year of construction. The value of buildings is obtained from property-valuation rolls and the content value is derived from insurance data. Damage and losses are calculated using Hazus software developed for FEMA. Losses are shown to be 53% higher than the SHM5 estimates.
{"title":"Influence of the 2020 Seismic Hazard Update on Residential Losses in Greater Montreal, Canada","authors":"Philippe Rosset, Xuejiao Long, Luc Chouinard","doi":"10.3390/geohazards4040023","DOIUrl":"https://doi.org/10.3390/geohazards4040023","url":null,"abstract":"Greater Montreal is situated in a region with moderate seismic activity and rests on soft ground deposits from the ancient Champlain Sea, as well as more recent alluvial deposits from the Saint Lawrence River. These deposits have the potential to amplify seismic waves, as demonstrated by past strong, and recent weak, earthquakes. Studies based on the 2015 National Seismic Hazard Model (SHM5) had estimated losses to residential buildings at 2% of their value for an event with a return period of 2475 years. In 2020, the seismic hazard model was updated (SHM6), resulting in more severe hazards for eastern Canada. This paper aims to quantify the impact of these changes on losses to residential buildings in Greater Montreal. Our exposure database includes population and buildings at the scale of dissemination areas (500–1000 inhabitants). Buildings are classified by occupancy and construction type and grouped into three building code levels based on year of construction. The value of buildings is obtained from property-valuation rolls and the content value is derived from insurance data. Damage and losses are calculated using Hazus software developed for FEMA. Losses are shown to be 53% higher than the SHM5 estimates.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135461393","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 : 2023-10-10DOI: 10.3390/geohazards4040022
George Papaioannou, Angelos Alamanos, Fotios Maris
Wildfires affect and change the burned sites’ condition, functionality, and ecosystem services. Altered hydrologic processes, such as runoff, increased streamflows, and sediment transport, are only a few examples resulting from burned soils, vegetation, and land cover. Such areas are flood-prone and face risks of extreme peak flows, reduced infiltration, water pollution affecting habitats, and hydromorphological changes. In this study, we present the different post-fire erosion and flood protection treatments that have been developed to avoid and mitigate the consequences and risks mentioned above. We categorize them into Land, Channel, Barrier, and Road treatments and analyze their types, such as cover-based methods, barriers, mulching, in-channel treatments, such as check dams, seeding, or even chemical treatments. Examples of how such treatments were used in real cases are provided, commenting on their results in flood and erosion protection. We found that cover changes were more effective than barriers, as they provided an immediate ground-cover increase in both Mediterranean and US sites. We explore the factors that play a role in their effectiveness, including storm duration and intensity, topography and slopes, land cover and uses, treatment implementation-installation, as well as fire-related factors such as burn severity. These factors have different effects on different treatments, so we further discuss the suitability of each one depending on the site’s and treatment’s characteristics. The outcomes of this work are expected to improve the understanding of the practical aspects of these treatments, providing for the first time a synthesis of the available knowledge on the multiple complex factors that can determine their efficiency.
{"title":"Evaluating Post-Fire Erosion and Flood Protection Techniques: A Narrative Review of Applications","authors":"George Papaioannou, Angelos Alamanos, Fotios Maris","doi":"10.3390/geohazards4040022","DOIUrl":"https://doi.org/10.3390/geohazards4040022","url":null,"abstract":"Wildfires affect and change the burned sites’ condition, functionality, and ecosystem services. Altered hydrologic processes, such as runoff, increased streamflows, and sediment transport, are only a few examples resulting from burned soils, vegetation, and land cover. Such areas are flood-prone and face risks of extreme peak flows, reduced infiltration, water pollution affecting habitats, and hydromorphological changes. In this study, we present the different post-fire erosion and flood protection treatments that have been developed to avoid and mitigate the consequences and risks mentioned above. We categorize them into Land, Channel, Barrier, and Road treatments and analyze their types, such as cover-based methods, barriers, mulching, in-channel treatments, such as check dams, seeding, or even chemical treatments. Examples of how such treatments were used in real cases are provided, commenting on their results in flood and erosion protection. We found that cover changes were more effective than barriers, as they provided an immediate ground-cover increase in both Mediterranean and US sites. We explore the factors that play a role in their effectiveness, including storm duration and intensity, topography and slopes, land cover and uses, treatment implementation-installation, as well as fire-related factors such as burn severity. These factors have different effects on different treatments, so we further discuss the suitability of each one depending on the site’s and treatment’s characteristics. The outcomes of this work are expected to improve the understanding of the practical aspects of these treatments, providing for the first time a synthesis of the available knowledge on the multiple complex factors that can determine their efficiency.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358360","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 : 2023-10-03DOI: 10.3390/geohazards4040021
Stefano Solarino, Elena Eva, Marco Anzidei, Gemma Musacchio, Maddalena De Lucia
Since the last century, global warming has been triggering sea level rise at an unprecedented rate. In the worst case climate scenario, sea level could rise by up to 1.1 m above the current level, causing coastal inundation and cascading effects, thus affecting about one billion people around the world. Though widespread and threatening, the phenomenon is not well known to citizens as it is often overshadowed by other effects of global warming. Here, we show the results of an online survey carried out in 2020–2021 to understand the level of citizens’ knowledge on sea level rise including causes, effects, exacerbation in response to land subsidence and best practice towards mitigation and adaptation. The most important result of the survey is that citizens believe that it is up to governments to take action to cope with the effects of rising sea levels or mitigate the rise itself. This occurs despite the survey showing that they actually know what individuals can do and that a failure to act poses a threat to society. Gaps and preconceptions need to be eradicated by strengthening the collaboration between scientists and schools to improve knowledge, empowering our society.
{"title":"Is Sea Level Rise a Known Threat? A Discussion Based on an Online Survey","authors":"Stefano Solarino, Elena Eva, Marco Anzidei, Gemma Musacchio, Maddalena De Lucia","doi":"10.3390/geohazards4040021","DOIUrl":"https://doi.org/10.3390/geohazards4040021","url":null,"abstract":"Since the last century, global warming has been triggering sea level rise at an unprecedented rate. In the worst case climate scenario, sea level could rise by up to 1.1 m above the current level, causing coastal inundation and cascading effects, thus affecting about one billion people around the world. Though widespread and threatening, the phenomenon is not well known to citizens as it is often overshadowed by other effects of global warming. Here, we show the results of an online survey carried out in 2020–2021 to understand the level of citizens’ knowledge on sea level rise including causes, effects, exacerbation in response to land subsidence and best practice towards mitigation and adaptation. The most important result of the survey is that citizens believe that it is up to governments to take action to cope with the effects of rising sea levels or mitigate the rise itself. This occurs despite the survey showing that they actually know what individuals can do and that a failure to act poses a threat to society. Gaps and preconceptions need to be eradicated by strengthening the collaboration between scientists and schools to improve knowledge, empowering our society.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740250","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 : 2023-09-26DOI: 10.3390/geohazards4040020
Adaku Jane Echendu
The rising incidence of flooding is a cause for global concern. Flooding is caused by both natural and human factors. In Nigeria, flooding has been attributed chiefly to human factors, such as poor waste disposal practices and management. Despite this known link, no empirical study is known to have engaged with urban residents to understand their actual waste disposal practices and ascertain their knowledge of the connection of their waste disposal practices to the flooding they are increasingly experiencing. This work fills this gap via an in-depth engagement with residents and experts on their waste disposal practices in the flood-prone city of Port Harcourt via a mixed-methods case study. Questionnaire surveys and qualitative interviews served as the primary data collection tools. The study confirms the poor waste practices of residents and provides empirical data on the prevalence of various forms of waste disposal practices. This provides key information that can guide the needed change in waste practices to eliminate this known flood driver in the pursuit of sustainable flood risk management. This is pertinent as waste management is one of the areas where citizens have agency to act. A behavioural shift is needed in this regard and must be encouraged via targeted public sensitization. Having local vanguards champion waste management behavioural turn is also recommended. The relevant authorities are encouraged to adopt a more sustainable approach to waste management by ensuring there are waste services and putting in place adequate disincentives to deter offenders.
{"title":"Flooding and Waste Disposal Practices of Urban Residents in Nigeria","authors":"Adaku Jane Echendu","doi":"10.3390/geohazards4040020","DOIUrl":"https://doi.org/10.3390/geohazards4040020","url":null,"abstract":"The rising incidence of flooding is a cause for global concern. Flooding is caused by both natural and human factors. In Nigeria, flooding has been attributed chiefly to human factors, such as poor waste disposal practices and management. Despite this known link, no empirical study is known to have engaged with urban residents to understand their actual waste disposal practices and ascertain their knowledge of the connection of their waste disposal practices to the flooding they are increasingly experiencing. This work fills this gap via an in-depth engagement with residents and experts on their waste disposal practices in the flood-prone city of Port Harcourt via a mixed-methods case study. Questionnaire surveys and qualitative interviews served as the primary data collection tools. The study confirms the poor waste practices of residents and provides empirical data on the prevalence of various forms of waste disposal practices. This provides key information that can guide the needed change in waste practices to eliminate this known flood driver in the pursuit of sustainable flood risk management. This is pertinent as waste management is one of the areas where citizens have agency to act. A behavioural shift is needed in this regard and must be encouraged via targeted public sensitization. Having local vanguards champion waste management behavioural turn is also recommended. The relevant authorities are encouraged to adopt a more sustainable approach to waste management by ensuring there are waste services and putting in place adequate disincentives to deter offenders.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960866","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}
ABSTRACTTunnels are a vital component of urban infrastructure that must be robust against seismic hazards. Given the randomness of earthquake occurrence, the seismic response of tunnel structures mut be studied by stochastic analysis methods. To this end, this study proposes a probability density evolution method (PDEM)-based framework to investigate the seismic performance of a circular tunnel under stochastic earthquake excitation. First, a suite of nonstationary earthquake motions compatible with the seismic design code was derived using a stochastic earthquake model. Then, a series of nonlinear dynamic numerical simulations were conducted for a typical circular tunnel that considers the soil-structure interaction. Finally, using the tunnel inclination angle as the performance index, the probability density function of the structural response of the tunnel was solved using the PDEM to obtain the corresponding exceedance probabilities of the tunnel under various damage states. The results show that the PDEM-based framework can be applied to evaluate the seismic performance of circular tunnels and could serve as a reference on the seismic fragility of tunnels and underground structures.KEYWORDS: Tunnelprobability density evolution methodstochastic dynamic responseexceedance probability Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was supported by National Key Research and Development Program of China (grant number 2022YFC3800905), National Natural Science Foundation of China (Grants No. 52108381, 52238010, 52090082), Innovation Program of Shanghai Municipal Education Commission (grant number 2019-01-07-00-07-456 E00051), Shanghai Science and Technology Committee Program (Grants No. 22dz1201202, 21dz1200601, 20dz1201404, 22XD1430200), and Natural Science Foundation of Chongqing, China (No. CSTB2023NSCQ-MSX0808).
{"title":"PDEM-based seismic performance evaluation of circular tunnels under stochastic earthquake excitation","authors":"Zhong-Kai Huang, Chao-Lie Ning, Dong-Mei Zhang, Hong-Wei Huang, Dong-Ming Zhang, Sotirios Argyroudis","doi":"10.1080/17499518.2023.2257171","DOIUrl":"https://doi.org/10.1080/17499518.2023.2257171","url":null,"abstract":"ABSTRACTTunnels are a vital component of urban infrastructure that must be robust against seismic hazards. Given the randomness of earthquake occurrence, the seismic response of tunnel structures mut be studied by stochastic analysis methods. To this end, this study proposes a probability density evolution method (PDEM)-based framework to investigate the seismic performance of a circular tunnel under stochastic earthquake excitation. First, a suite of nonstationary earthquake motions compatible with the seismic design code was derived using a stochastic earthquake model. Then, a series of nonlinear dynamic numerical simulations were conducted for a typical circular tunnel that considers the soil-structure interaction. Finally, using the tunnel inclination angle as the performance index, the probability density function of the structural response of the tunnel was solved using the PDEM to obtain the corresponding exceedance probabilities of the tunnel under various damage states. The results show that the PDEM-based framework can be applied to evaluate the seismic performance of circular tunnels and could serve as a reference on the seismic fragility of tunnels and underground structures.KEYWORDS: Tunnelprobability density evolution methodstochastic dynamic responseexceedance probability Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was supported by National Key Research and Development Program of China (grant number 2022YFC3800905), National Natural Science Foundation of China (Grants No. 52108381, 52238010, 52090082), Innovation Program of Shanghai Municipal Education Commission (grant number 2019-01-07-00-07-456 E00051), Shanghai Science and Technology Committee Program (Grants No. 22dz1201202, 21dz1200601, 20dz1201404, 22XD1430200), and Natural Science Foundation of Chongqing, China (No. CSTB2023NSCQ-MSX0808).","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135010754","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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