Pub Date : 2023-01-02DOI: 10.1080/17499518.2023.2184479
Akihiro Shioi, Y. Otake, I. Yoshida, S. Muramatsu, S. Ohno
ABSTRACT The application of data science technologies in geotechnical and earthquake engineering is a hot topic. This study aimed to identify the macroscopic dynamic properties of the soil from the previous records of seismic motions observed at the ground surface utilizing the dynamic mode decomposition (DMD). The key to our ingenuity was to replace the soil layer composition and dynamic properties with a single-degree-of-freedom (SDOF) vibration model based on the ground surface observation records. In the validation process, first, a comparison was made between the proposed method and the analytical solution for an SDOF vibration system; second, a comparison was made with a one-dimensional equivalent linear multiple reflection theory analysis considering the nonlinear soil profile. The proposed method effectively approximated complex ground profiles to an equivalent SDOF vibration system and constructed shear strain-dependent models of the macroscopic pseudo-shear modulus and damping constant from the observed ground surface seismic motions. This study was based on numerical experiments and limited conditions of small seismic amplitudes for which equivalent linear approximations could be made. Based on the results obtained in this paper, we aim to extend the model to wide-area forecasting by improving it to a practical model that covers strong nonlinearities.
{"title":"Data-driven approximation of geotechnical dynamics to an equivalent single-degree-of-freedom vibration system based on dynamic mode decomposition","authors":"Akihiro Shioi, Y. Otake, I. Yoshida, S. Muramatsu, S. Ohno","doi":"10.1080/17499518.2023.2184479","DOIUrl":"https://doi.org/10.1080/17499518.2023.2184479","url":null,"abstract":"ABSTRACT The application of data science technologies in geotechnical and earthquake engineering is a hot topic. This study aimed to identify the macroscopic dynamic properties of the soil from the previous records of seismic motions observed at the ground surface utilizing the dynamic mode decomposition (DMD). The key to our ingenuity was to replace the soil layer composition and dynamic properties with a single-degree-of-freedom (SDOF) vibration model based on the ground surface observation records. In the validation process, first, a comparison was made between the proposed method and the analytical solution for an SDOF vibration system; second, a comparison was made with a one-dimensional equivalent linear multiple reflection theory analysis considering the nonlinear soil profile. The proposed method effectively approximated complex ground profiles to an equivalent SDOF vibration system and constructed shear strain-dependent models of the macroscopic pseudo-shear modulus and damping constant from the observed ground surface seismic motions. This study was based on numerical experiments and limited conditions of small seismic amplitudes for which equivalent linear approximations could be made. Based on the results obtained in this paper, we aim to extend the model to wide-area forecasting by improving it to a practical model that covers strong nonlinearities.","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":"45079948","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 : 2023-01-02DOI: 10.1080/17499518.2023.2169942
Chao Shi, Yu Wang
ABSTRACT Image-based stochastic simulation methods, such as multiple point statistics (MPS), can be viewed as a physics-informed Bayesian learning approach, which samples typical stratigraphic patterns from a single training image for onward conditional modelling of subsurface stratigraphy. A training image is essentially a prior geological model, which comprises representative stratigraphic connectivity at the site of interest. One key difficulty hindering wide application of image-based geological modelling methods is the lack of qualified training images. In this study, a systematic framework is proposed to develop training image databases for conditional simulations of subsurface stratigraphy. Collected training images can be further categorised based on three key factors, namely, geological origin, site location and application scenario. As a pilot study, a total of 54 geological cross-sections, mainly interpreted by experienced engineering practitioners, for weathered granite and tuff slopes in Hong Kong are collected and compiled as two training image databases. To demonstrate value and application of the established training image databases, subsurface stratigraphy for real weathered granite slope examples are used as illustrative examples, and stratigraphic uncertainty is also quantified. Results indicate that training image databases are of great significance for subsurface stratigraphy and uncertainty quantification, particularly when only limited site-specific data are available.
{"title":"Development of training image database for subsurface stratigraphy","authors":"Chao Shi, Yu Wang","doi":"10.1080/17499518.2023.2169942","DOIUrl":"https://doi.org/10.1080/17499518.2023.2169942","url":null,"abstract":"ABSTRACT Image-based stochastic simulation methods, such as multiple point statistics (MPS), can be viewed as a physics-informed Bayesian learning approach, which samples typical stratigraphic patterns from a single training image for onward conditional modelling of subsurface stratigraphy. A training image is essentially a prior geological model, which comprises representative stratigraphic connectivity at the site of interest. One key difficulty hindering wide application of image-based geological modelling methods is the lack of qualified training images. In this study, a systematic framework is proposed to develop training image databases for conditional simulations of subsurface stratigraphy. Collected training images can be further categorised based on three key factors, namely, geological origin, site location and application scenario. As a pilot study, a total of 54 geological cross-sections, mainly interpreted by experienced engineering practitioners, for weathered granite and tuff slopes in Hong Kong are collected and compiled as two training image databases. To demonstrate value and application of the established training image databases, subsurface stratigraphy for real weathered granite slope examples are used as illustrative examples, and stratigraphic uncertainty is also quantified. Results indicate that training image databases are of great significance for subsurface stratigraphy and uncertainty quantification, particularly when only limited site-specific data are available.","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":"42431673","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 : 2023-01-02DOI: 10.1080/17499518.2023.2172187
Zhong Zhou, Long Yan, Junjie Zhang, Hao Yang
ABSTRACT To solve slow speed and low accuracy of traditional detection methods of tunnel lining cracks, especially under the complicated situation of tunnel in operation, this work proposed an improved You Only Look Once version X (YOLOX) tunnel lining crack image detection algorithm. First, Mobilenetv3 was used to replace YOLOX’s CSPDarknet network. The Efficient Channel Attention (ECA) module was then added to the enhanced feature extraction network, and the IOU loss function was replaced by the generalised IOU (GIOU) loss function. A tunnel crack image data set was constructed and used to compare the performance of the improved YOLOX algorithm with that of five other algorithms. The improved YOLOX algorithm solves the shortcomings of the other five algorithms. The results showed that the improved YOLOX algorithm had 82.48% F1 score and 87.28% AP value, which is higher than that of the other five algorithms at varying degrees. In addition, the data size of the improved YOLOX model was 51.2 M, which is 75.27% compressed compared to the YOLOX model. The time was 16.52 ms, and the FPS was 60.52 frames/s. Therefore, the proposed improved YOLOX algorithm can realise the high-speed, high-precision, real-time dynamic detection of tunnel lining cracks in complicated environments.
针对传统隧道衬砌裂缝检测方法速度慢、精度低的问题，特别是在隧道运行复杂的情况下，本文提出了一种改进的You Only Look Once version X (YOLOX)隧道衬砌裂缝图像检测算法。首先，Mobilenetv3被用来取代YOLOX的CSPDarknet网络。然后将有效通道注意(ECA)模块添加到增强的特征提取网络中，并将IOU损失函数替换为广义IOU (GIOU)损失函数。构建了隧道裂缝图像数据集，并将改进的YOLOX算法与其他五种算法的性能进行了比较。改进的YOLOX算法解决了其他五种算法的不足。结果表明，改进后的YOLOX算法F1得分为82.48%，AP值为87.28%，不同程度地高于其他5种算法。此外，改进YOLOX模型的数据量为51.2 M，与YOLOX模型相比压缩了75.27%。时间为16.52 ms，帧数为60.52帧/秒。因此，本文提出的改进YOLOX算法可以实现复杂环境下隧道衬砌裂缝的高速、高精度、实时动态检测。
{"title":"Real-time tunnel lining crack detection based on an improved You Only Look Once version X algorithm","authors":"Zhong Zhou, Long Yan, Junjie Zhang, Hao Yang","doi":"10.1080/17499518.2023.2172187","DOIUrl":"https://doi.org/10.1080/17499518.2023.2172187","url":null,"abstract":"ABSTRACT To solve slow speed and low accuracy of traditional detection methods of tunnel lining cracks, especially under the complicated situation of tunnel in operation, this work proposed an improved You Only Look Once version X (YOLOX) tunnel lining crack image detection algorithm. First, Mobilenetv3 was used to replace YOLOX’s CSPDarknet network. The Efficient Channel Attention (ECA) module was then added to the enhanced feature extraction network, and the IOU loss function was replaced by the generalised IOU (GIOU) loss function. A tunnel crack image data set was constructed and used to compare the performance of the improved YOLOX algorithm with that of five other algorithms. The improved YOLOX algorithm solves the shortcomings of the other five algorithms. The results showed that the improved YOLOX algorithm had 82.48% F1 score and 87.28% AP value, which is higher than that of the other five algorithms at varying degrees. In addition, the data size of the improved YOLOX model was 51.2 M, which is 75.27% compressed compared to the YOLOX model. The time was 16.52 ms, and the FPS was 60.52 frames/s. Therefore, the proposed improved YOLOX algorithm can realise the high-speed, high-precision, real-time dynamic detection of tunnel lining cracks in complicated environments.","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":"42525575","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 : 2023-01-02DOI: 10.1080/17499518.2023.2169941
Wei Yan, Yu Yan, Ping Shen, Wanqi Zhou
ABSTRACT Due to complex interactions between immersed tunnel and surrounding environment, it is difficult to apply theoretical analysis for axial displacement (DIS) of immersion joints. To develop a generalised model for DIS prediction, Deep Neural Network (DNN) could be considered. However, the spatial generalisation of black-box DNN models is not always convincible for small data. In this study, we proposed a novel hybrid physical data (HPD) informed DNN model with improved spatial generalisation for prediction of DIS. The physical mechanism of DIS is firstly analysed by correlation between DIS and other monitoring data. The HPD is then created based on the physical analysis and contributes to the DNN as a substituting feature rather than an additional feature. Three DNN models fed with different groups of features are compared, while the proposed HPD-DNN has outperformed others in terms of both prediction generalisation as well as accuracy. The permutation feature importance analysis reveals that HPD has effectively enhanced physical interpretation of DNN, which supports the results stated in physical analysis. The application of HPD is further verified to enhance the spatial generalisation of prediction for not only DNN but also other black-box models, which is promising for insufficient data problems in geotechnical engineering.
{"title":"A hybrid physical data informed DNN in axial displacement prediction of immersed tunnel joint","authors":"Wei Yan, Yu Yan, Ping Shen, Wanqi Zhou","doi":"10.1080/17499518.2023.2169941","DOIUrl":"https://doi.org/10.1080/17499518.2023.2169941","url":null,"abstract":"ABSTRACT Due to complex interactions between immersed tunnel and surrounding environment, it is difficult to apply theoretical analysis for axial displacement (DIS) of immersion joints. To develop a generalised model for DIS prediction, Deep Neural Network (DNN) could be considered. However, the spatial generalisation of black-box DNN models is not always convincible for small data. In this study, we proposed a novel hybrid physical data (HPD) informed DNN model with improved spatial generalisation for prediction of DIS. The physical mechanism of DIS is firstly analysed by correlation between DIS and other monitoring data. The HPD is then created based on the physical analysis and contributes to the DNN as a substituting feature rather than an additional feature. Three DNN models fed with different groups of features are compared, while the proposed HPD-DNN has outperformed others in terms of both prediction generalisation as well as accuracy. The permutation feature importance analysis reveals that HPD has effectively enhanced physical interpretation of DNN, which supports the results stated in physical analysis. The application of HPD is further verified to enhance the spatial generalisation of prediction for not only DNN but also other black-box models, which is promising for insufficient data problems in geotechnical engineering.","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":"42721168","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 : 2023-01-02DOI: 10.1080/17499518.2023.2172188
X. Tan, Wei-zhong Chen, Changkun Qin, Wusheng Zhao, Wei Ye
ABSTRACT The study of mining-induced stress is essential to ensure the safety production of coalmine. Due to the limited number of monitoring points and local monitoring area, the perception of structure status is insufficient. This study aims to present a deep learning (DL) model to derive the stress distribution characteristics of the overall coalmine roof. First, the framework of spatial deduction model termed as transferring convolutional neural network (TCNN) is presented, where the convolutional neural network is transferred on different datasets. According to this framework, the spatial correlations of structural mechanical responses at different heights above roadway roof are learned through numerical simulation. Subsequently, the learned results are transferred to monitoring data to derive the actual state of the overall roof. In order to verify the reliability of the TCNN model, the stress sensor is installed in the derived plane to collect the actual data, and two indicators are adopted to evaluate the reasonability of deduction results. Experimental results indicated that 92.25% features of mining-induced stress distribution are captured by the TCNN model and the deduction error is 2.037 MPa. Therefore, the presented model is reliable to obtain the overall mechanical state of the coalmine roof, and it is supposed to promote the application of DL in underground construction.
{"title":"Characterisation for spatial distribution of mining-induced stress through deep learning algorithm on SHM data","authors":"X. Tan, Wei-zhong Chen, Changkun Qin, Wusheng Zhao, Wei Ye","doi":"10.1080/17499518.2023.2172188","DOIUrl":"https://doi.org/10.1080/17499518.2023.2172188","url":null,"abstract":"ABSTRACT The study of mining-induced stress is essential to ensure the safety production of coalmine. Due to the limited number of monitoring points and local monitoring area, the perception of structure status is insufficient. This study aims to present a deep learning (DL) model to derive the stress distribution characteristics of the overall coalmine roof. First, the framework of spatial deduction model termed as transferring convolutional neural network (TCNN) is presented, where the convolutional neural network is transferred on different datasets. According to this framework, the spatial correlations of structural mechanical responses at different heights above roadway roof are learned through numerical simulation. Subsequently, the learned results are transferred to monitoring data to derive the actual state of the overall roof. In order to verify the reliability of the TCNN model, the stress sensor is installed in the derived plane to collect the actual data, and two indicators are adopted to evaluate the reasonability of deduction results. Experimental results indicated that 92.25% features of mining-induced stress distribution are captured by the TCNN model and the deduction error is 2.037 MPa. Therefore, the presented model is reliable to obtain the overall mechanical state of the coalmine roof, and it is supposed to promote the application of DL in underground construction.","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":"44526003","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 : 2023-01-02DOI: 10.1080/17499518.2023.2182889
J. Morgenroth, K. Kalenchuk, L. Moreau-Verlaan, M. Perras, U. T. Khan
ABSTRACT Digitalisation has increased access to large amounts of data for rock engineers. Machine learning presents an opportunity to aid data interpretation. The operators of Garson Mine use a microseismic database to calibrate a mine-scale finite difference model, used to assess seismic risk to inform mine operations. A Long-Short Term Memory (LSTM) network is proposed for stress model updating. The model is trained using microseismic data, geology, and geomechanical parameters from the FLAC3D model. Two LSTM networks are developed for Garson Mine: (1) predicting far field principal stresses in the FLAC3D model, and (2) predicting the far field six-component stress tensors in the model. Various LSTM network hyperparameters were analyzed to determine the architecture for the targets: input encoding and pre-processing, training solver, network layer architecture, and cost function. Architectures were chosen based on the corrected Akaike Information Criterion (AICc), coefficient of determination (R2), and percent capture (%C). When predicting principal stresses, AICc = −59.62, R2 = 0.996, and %C = 97%, and when predicting the six-component stress tensor AICc = −45.50, R2 = 0.997, and %C = 80%. This research represents progress towards continuous, automated updating of numerical models such that rapid, more accurate forecasts of changes in stress conditions will allow earlier reaction to challenging stress environments, increasing safety of excavations.
{"title":"A novel long-short term memory network approach for stress model updating for excavations in high stress environments","authors":"J. Morgenroth, K. Kalenchuk, L. Moreau-Verlaan, M. Perras, U. T. Khan","doi":"10.1080/17499518.2023.2182889","DOIUrl":"https://doi.org/10.1080/17499518.2023.2182889","url":null,"abstract":"ABSTRACT Digitalisation has increased access to large amounts of data for rock engineers. Machine learning presents an opportunity to aid data interpretation. The operators of Garson Mine use a microseismic database to calibrate a mine-scale finite difference model, used to assess seismic risk to inform mine operations. A Long-Short Term Memory (LSTM) network is proposed for stress model updating. The model is trained using microseismic data, geology, and geomechanical parameters from the FLAC3D model. Two LSTM networks are developed for Garson Mine: (1) predicting far field principal stresses in the FLAC3D model, and (2) predicting the far field six-component stress tensors in the model. Various LSTM network hyperparameters were analyzed to determine the architecture for the targets: input encoding and pre-processing, training solver, network layer architecture, and cost function. Architectures were chosen based on the corrected Akaike Information Criterion (AICc), coefficient of determination (R2), and percent capture (%C). When predicting principal stresses, AICc = −59.62, R2 = 0.996, and %C = 97%, and when predicting the six-component stress tensor AICc = −45.50, R2 = 0.997, and %C = 80%. This research represents progress towards continuous, automated updating of numerical models such that rapid, more accurate forecasts of changes in stress conditions will allow earlier reaction to challenging stress environments, increasing safety of excavations.","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":"42021511","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 : 2023-01-02DOI: 10.1080/17499518.2023.2181973
T. Shuku, K. Phoon
ABSTRACT This paper presents a method of subsurface modelling based on a Markov random field (MRF) model called Potts model. Potts model is an undirected graphical model and has been applied in image processing such as image denoising, restoration and inpainting. The proposed method is simple and requires only a few borehole data on soil types in both training and inference stages. Current implementations of the Potts model require substantial data for training, and they are not suitable for subsurface modelling. The proposed method was demonstrated through numerical examples for 2D and 3D virtual grounds and a real case history. In the numerical examples, the effect of the number of training datasets on the estimation results was also investigated. The proposed method can provide not only the most probable inference of subsurface model but also the spatial distribution of geological uncertainty and is compatible with reliability-based analysis in geotechnical engineering. The spatial distribution of uncertainty is informative in its own right. It directs the engineer to focus on mechanically important zones where the critical failure mechanism passes through if they coincide with the low-accuracy zones.
{"title":"Data-driven subsurface modelling using a Markov random field model","authors":"T. Shuku, K. Phoon","doi":"10.1080/17499518.2023.2181973","DOIUrl":"https://doi.org/10.1080/17499518.2023.2181973","url":null,"abstract":"ABSTRACT This paper presents a method of subsurface modelling based on a Markov random field (MRF) model called Potts model. Potts model is an undirected graphical model and has been applied in image processing such as image denoising, restoration and inpainting. The proposed method is simple and requires only a few borehole data on soil types in both training and inference stages. Current implementations of the Potts model require substantial data for training, and they are not suitable for subsurface modelling. The proposed method was demonstrated through numerical examples for 2D and 3D virtual grounds and a real case history. In the numerical examples, the effect of the number of training datasets on the estimation results was also investigated. The proposed method can provide not only the most probable inference of subsurface model but also the spatial distribution of geological uncertainty and is compatible with reliability-based analysis in geotechnical engineering. The spatial distribution of uncertainty is informative in its own right. It directs the engineer to focus on mechanically important zones where the critical failure mechanism passes through if they coincide with the low-accuracy zones.","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":"49438394","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 : 2023-01-01DOI: 10.1007/978-981-99-3955-8
{"title":"Geohazards: Analysis, Modelling and Forecasting","authors":"","doi":"10.1007/978-981-99-3955-8","DOIUrl":"https://doi.org/10.1007/978-981-99-3955-8","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":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80351928","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 : 2022-12-23DOI: 10.3390/geohazards4010001
N. Evelpidou, Maria Tzouxanioti, E. Spyrou, A. Petropoulos, A. Karkani, G. Saitis, Markos Margaritis
Greece, like the rest of the Mediterranean countries, faces wildland fires every year. Besides their short-term socioeconomic impacts, ecological destruction, and loss of human lives, forest fires also increase the burnt areas’ risk of flash flood phenomena, as the vegetation, which acted in a protective way against runoff and soil erosion, is massively removed. Among the most severe wildland fire events in Greece were those of summer 2021, which were synchronous to the very severe heat waves that hit the broader area of the Balkan Peninsula. More than 3600 km2 of land was burnt and a significant amount of natural vegetation removed. Three of the burnt areas are examined in this work, namely, Attica, Northern Euboea, and the Peloponnese, in order to assess their risk of future flash flood events. The burnt areas were mapped, and their geological and geomorphological features studied. Flash flood hazard assessment was accomplished through a Boolean logic-based model applied through Geographic Information Systems (GIS) software, which allowed the prioritization of the requirement for protection by identifying which locations were most prone to flooding. The largest part of our study areas is characterized by geomorphological and geological conditions that facilitate flash flood events. According to our findings, in almost all study areas, the regions downstream of the burnt areas present high to very high flash flood hazard, due to their geomorphological and geological features (slope, drainage density, and hydrolithology). The only areas that were found to be less prone to flood events were Vilia and Varimpompi (Attica), due to their gentler slope inclinations and overall geomorphological characteristics. It is known that vegetation cover acts protectively against flash floods. However, in this case, large areas were severely burnt and vegetation is absent, resulting in the appearance of flash floods. Moreover, imminent flooding events are expected to be even more intense in the areas downstream of the burnt regions, possibly bearing even worse impacts on the local population, infrastructure, etc.
{"title":"GIS-Based Assessment of Fire Effects on Flash Flood Hazard: The Case of the Summer 2021 Forest Fires in Greece","authors":"N. Evelpidou, Maria Tzouxanioti, E. Spyrou, A. Petropoulos, A. Karkani, G. Saitis, Markos Margaritis","doi":"10.3390/geohazards4010001","DOIUrl":"https://doi.org/10.3390/geohazards4010001","url":null,"abstract":"Greece, like the rest of the Mediterranean countries, faces wildland fires every year. Besides their short-term socioeconomic impacts, ecological destruction, and loss of human lives, forest fires also increase the burnt areas’ risk of flash flood phenomena, as the vegetation, which acted in a protective way against runoff and soil erosion, is massively removed. Among the most severe wildland fire events in Greece were those of summer 2021, which were synchronous to the very severe heat waves that hit the broader area of the Balkan Peninsula. More than 3600 km2 of land was burnt and a significant amount of natural vegetation removed. Three of the burnt areas are examined in this work, namely, Attica, Northern Euboea, and the Peloponnese, in order to assess their risk of future flash flood events. The burnt areas were mapped, and their geological and geomorphological features studied. Flash flood hazard assessment was accomplished through a Boolean logic-based model applied through Geographic Information Systems (GIS) software, which allowed the prioritization of the requirement for protection by identifying which locations were most prone to flooding. The largest part of our study areas is characterized by geomorphological and geological conditions that facilitate flash flood events. According to our findings, in almost all study areas, the regions downstream of the burnt areas present high to very high flash flood hazard, due to their geomorphological and geological features (slope, drainage density, and hydrolithology). The only areas that were found to be less prone to flood events were Vilia and Varimpompi (Attica), due to their gentler slope inclinations and overall geomorphological characteristics. It is known that vegetation cover acts protectively against flash floods. However, in this case, large areas were severely burnt and vegetation is absent, resulting in the appearance of flash floods. Moreover, imminent flooding events are expected to be even more intense in the areas downstream of the burnt regions, possibly bearing even worse impacts on the local population, infrastructure, etc.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72401691","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 : 2022-12-15DOI: 10.1080/17499518.2022.2149814
Y. Shin, N. Bozorgzadeh, Zhong-qiang Liu, F. Nadim, Jaehyu Park, M. Chung
ABSTRACT Ground conditions comprised of slightly or completely weathered rock are frequently encountered in design of bridge foundations in Korea. It is rather challenging to assess the accuracy of the common design methodologies for shallow foundations in these ground conditions as the foundation bearing capacity depends on the degree of weathering. This paper presents reliability-based derivation of resistance factors for the bearing capacity of shallow foundations on slightly and completely weathered rock using field plate load test data. More than 140 plate load tests were performed at 52 sites, 33 of which were considered to be of high quality and reliable. These high-quality tests were used to evaluate the uncertainties associated with the bearing capacity equations, and the resistance factors corresponding to current prescribed load factors. A reliability-based approach, with target annual failure probabilities of 1.0 × 10−3, 2.0 × 10−4, 1.0 × 10−4, was adopted to estimate the required resistance factors for different design equations. A Bayesian approach was adopted to facilitate quantification and propagation of statistical parameter uncertainty due to limited available data. The best estimates of the calibrated resistance factors range from 0.40 to about 0.47 for the considered target reliability levels, which is in good agreement with currently used values in Korea.
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