Pub Date : 2024-02-08DOI: 10.1177/87552930231225983
S. Pezeshk, Christie Assadollahi, A. Zandieh
The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.
本研究的主要目的是估算北美中部和东部（CENA）的地震学参数，包括几何展布、非弹性衰减、应力参数和场地衰减参数。在本研究中，我们使用粒子群优化（PSO）反演下一代衰减-东部（NGA-East）地动模型（GMMs）预测的 5%-阻尼伪谱加速度（PSA）中位数的加权平均值，从而开发出具有约束良好的地动参数集的点源随机 GMM。针对震级范围 M = 4.0-8.0、断裂距离 Rrup = 1-1000 km、周期 T = 0.01-10 s 和国家地震灾害减灾计划场地等级 A 条件进行了特定震级反演。该研究结果产生了一个单一的随机 GMM，其 PSA 值与 NGA-East GMM 的中值相似。本研究得出的参数可用于混合经验法（HEM）应用。本研究首次使用为 NGA 东部项目开发的 GMM 进行正式反演。利用 NGA-West2 GMM 得出的模拟中、小震级和大震级数据对该方法进行了验证。
{"title":"An equivalent point-source stochastic model of the NGA-East ground-motion models","authors":"S. Pezeshk, Christie Assadollahi, A. Zandieh","doi":"10.1177/87552930231225983","DOIUrl":"https://doi.org/10.1177/87552930231225983","url":null,"abstract":"The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/87552930241228554
Bruce Maison, Shih-Ho Chao
This is a Discussion of the following article: Cook D., et al. (2023). ASCE/SEI 41 assessment of reinforced concrete buildings: Benchmarking nonlinear dynamic procedures with empirical damage observations, Earthquake Spectra, August 2023, Vol. 39, No. 3, pp. 1721–1754.
本文是对以下文章的讨论：Cook D., et al. (2023).ASCE/SEI 41 钢筋混凝土建筑评估：Benchmarking nonlinear dynamic procedures with empirical damage observations, Earthquake Spectra, August 2023, Vol. 39, No. 3, pp.
{"title":"Discussion of “ASCE/SEI 41 assessment of reinforced concrete buildings: Benchmarking nonlinear dynamic procedures with empirical damage observations”","authors":"Bruce Maison, Shih-Ho Chao","doi":"10.1177/87552930241228554","DOIUrl":"https://doi.org/10.1177/87552930241228554","url":null,"abstract":"This is a Discussion of the following article: Cook D., et al. (2023). ASCE/SEI 41 assessment of reinforced concrete buildings: Benchmarking nonlinear dynamic procedures with empirical damage observations, Earthquake Spectra, August 2023, Vol. 39, No. 3, pp. 1721–1754.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":" 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/87552930231225983
S. Pezeshk, Christie Assadollahi, A. Zandieh
The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.
本研究的主要目的是估算北美中部和东部（CENA）的地震学参数，包括几何展布、非弹性衰减、应力参数和场地衰减参数。在本研究中，我们使用粒子群优化（PSO）反演下一代衰减-东部（NGA-East）地动模型（GMMs）预测的 5%-阻尼伪谱加速度（PSA）中位数的加权平均值，从而开发出具有约束良好的地动参数集的点源随机 GMM。针对震级范围 M = 4.0-8.0、断裂距离 Rrup = 1-1000 km、周期 T = 0.01-10 s 和国家地震灾害减灾计划场地等级 A 条件进行了特定震级反演。该研究结果产生了一个单一的随机 GMM，其 PSA 值与 NGA-East GMM 的中值相似。本研究得出的参数可用于混合经验法（HEM）应用。本研究首次使用为 NGA 东部项目开发的 GMM 进行正式反演。利用 NGA-West2 GMM 得出的模拟中、小震级和大震级数据对该方法进行了验证。
{"title":"An equivalent point-source stochastic model of the NGA-East ground-motion models","authors":"S. Pezeshk, Christie Assadollahi, A. Zandieh","doi":"10.1177/87552930231225983","DOIUrl":"https://doi.org/10.1177/87552930231225983","url":null,"abstract":"The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"10 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139851510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).
{"title":"A building imagery database for the calibration of machine learning algorithms","authors":"Vitor Silva, Romain Sousa, Feliz Ribeiro Gouveia, Jorge Lopes, Maria Guerreiro","doi":"10.1177/87552930241229103","DOIUrl":"https://doi.org/10.1177/87552930241229103","url":null,"abstract":"In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":" 61","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).
{"title":"A building imagery database for the calibration of machine learning algorithms","authors":"Vitor Silva, Romain Sousa, Feliz Ribeiro Gouveia, Jorge Lopes, Maria Guerreiro","doi":"10.1177/87552930241229103","DOIUrl":"https://doi.org/10.1177/87552930241229103","url":null,"abstract":"In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1177/87552930231223749
Ali Lashgari, R. Moss
The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.
{"title":"Displacement and damage analysis of earth dams during the 2023 Turkiye earthquake sequence","authors":"Ali Lashgari, R. Moss","doi":"10.1177/87552930231223749","DOIUrl":"https://doi.org/10.1177/87552930231223749","url":null,"abstract":"The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139889721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1177/87552930231223749
Ali Lashgari, R. Moss
The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.
{"title":"Displacement and damage analysis of earth dams during the 2023 Turkiye earthquake sequence","authors":"Ali Lashgari, R. Moss","doi":"10.1177/87552930231223749","DOIUrl":"https://doi.org/10.1177/87552930231223749","url":null,"abstract":"The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"41 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139830057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.1177/87552930231222769
Emily Mongold, R. Costa, Ádám Zsarnóczay, J. Baker
Post-disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single-family owner-occupied housing, while empirical evidence shows that rental units and multi-family housing are disadvantaged in post-disaster recovery. To fill this gap, this article presents an agent-based housing recovery model that includes the four common type–tenure combinations of single- and multi-family owner- and renter-occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non-recovery of multi-family housing than single-family housing, as observed in past disasters, and a heavy reliance of single-family renter-occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi-family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi-family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG-DR). This benefit is a result of modeling the financing sources, that CDBG-DR is available, and that many multi-family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds.
{"title":"Modeling post-disaster recovery: Accounting for rental and multi-family housing","authors":"Emily Mongold, R. Costa, Ádám Zsarnóczay, J. Baker","doi":"10.1177/87552930231222769","DOIUrl":"https://doi.org/10.1177/87552930231222769","url":null,"abstract":"Post-disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single-family owner-occupied housing, while empirical evidence shows that rental units and multi-family housing are disadvantaged in post-disaster recovery. To fill this gap, this article presents an agent-based housing recovery model that includes the four common type–tenure combinations of single- and multi-family owner- and renter-occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non-recovery of multi-family housing than single-family housing, as observed in past disasters, and a heavy reliance of single-family renter-occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi-family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi-family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG-DR). This benefit is a result of modeling the financing sources, that CDBG-DR is available, and that many multi-family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1177/87552930231217002
Kaleigh M. Yost, A. Yerro, Eileen R Martin, Russell A Green
Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance ( q m) and true tip resistance ( q t) data, where q m is the tip resistance recorded by the CPT in a layered profile, and q t represents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q m and q t pairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q m and q t are provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development.
锥入度试验（CPT）是一种常用的现场土壤表征方法。记录的数据可用于各种用途，包括地震诱发的液化评估。然而，CPT 在特定深度记录的数据会受到锥尖周围影响区内所有土壤特性的影响，而非仅受该特定深度土壤特性的影响。这会导致分层区域的数据模糊或平均化，这种现象被称为多重薄层效应。多重薄层效应会导致对薄层、夹层的厚度和刚度表征不准确。已经提出了一些修正程序来调整 CPT 顶端阻力，以适应多重薄层效应，但许多程序随着层厚度的减小而变得不那么有效。要比较或改进这些程序并开发新的程序，关键是要有成对的测量顶端阻力（q m）和真实顶端阻力（q t）数据，其中 q m 是 CPT 在分层剖面中记录的顶端阻力，q t 代表在没有多薄层效应的剖面中测量的顶端阻力。遗憾的是，包含 q m 和 q t 对的数据集极为罕见。因此，本文提供了一个独特的数据库，其中包含 49 个高度互层土壤剖面的实验室和数值生成的 CPT 数据。每个剖面都提供了 q m 和 q t。随附的 Jupyter 笔记本可方便使用这些数据，并为未来的统计学习（或其他）应用做好准备，以支持多薄层校正程序的开发。
{"title":"A cone penetration test database for multiple thin-layer correction procedure development","authors":"Kaleigh M. Yost, A. Yerro, Eileen R Martin, Russell A Green","doi":"10.1177/87552930231217002","DOIUrl":"https://doi.org/10.1177/87552930231217002","url":null,"abstract":"Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance ( q m) and true tip resistance ( q t) data, where q m is the tip resistance recorded by the CPT in a layered profile, and q t represents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q m and q t pairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q m and q t are provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"37 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1177/87552930231215723
Okan Ilhan, Youssef M. Hashash, J. Stewart, E. Rathje, Sissy Nikolaou, Ken Campbell
This article presents a suite of response spectrum (RS) and Fourier amplitude spectrum (FAS) site amplification models for Central and Eastern North America (CENA). The amplification database used in model development was produced through large-scale one-dimensional site response analyses and overcomes limitations of prior databases by providing broader coverage of anticipated site conditions. New amplification functions conditioned on either VS30 or site natural period ( Tnat) as the primary independent variable are provided, the latter of which better captures features of the computed site amplification from the simulated database (i.e. model dispersion is reduced). Models for standard deviation also are developed. RS and FAS linear amplification functions that adjust the reference condition of VS = 3000 m/s to VS30 = 760 m/s, and account for amplification for profiles either with a sharp VS impedance or a more gradual increase in VS are also developed.
本文介绍了一套北美中部和东部（CENA）的响应谱（RS）和傅立叶振幅谱（FAS）场地放大模型。模型开发中使用的放大数据库是通过大规模一维场地响应分析生成的，通过提供更广泛的预期场地条件覆盖范围，克服了以前数据库的局限性。提供了以 VS30 或场地自然周期（Tnat）为主要自变量的新放大函数，后者能更好地捕捉模拟数据库中计算出的场地放大特征（即模型离散性减小）。此外，还建立了标准偏差模型。此外，还开发了 RS 和 FAS 线性放大函数，将 VS = 3000 m/s 的参考条件调整为 VS30 = 760 m/s，并考虑了 VS 阻抗急剧增加或 VS 逐渐增加的剖面放大。
{"title":"Simulated site amplification for Central and Eastern North America: Data set development and amplification models","authors":"Okan Ilhan, Youssef M. Hashash, J. Stewart, E. Rathje, Sissy Nikolaou, Ken Campbell","doi":"10.1177/87552930231215723","DOIUrl":"https://doi.org/10.1177/87552930231215723","url":null,"abstract":"This article presents a suite of response spectrum (RS) and Fourier amplitude spectrum (FAS) site amplification models for Central and Eastern North America (CENA). The amplification database used in model development was produced through large-scale one-dimensional site response analyses and overcomes limitations of prior databases by providing broader coverage of anticipated site conditions. New amplification functions conditioned on either VS30 or site natural period ( Tnat) as the primary independent variable are provided, the latter of which better captures features of the computed site amplification from the simulated database (i.e. model dispersion is reduced). Models for standard deviation also are developed. RS and FAS linear amplification functions that adjust the reference condition of VS = 3000 m/s to VS30 = 760 m/s, and account for amplification for profiles either with a sharp VS impedance or a more gradual increase in VS are also developed.","PeriodicalId":505879,"journal":{"name":"Earthquake Spectra","volume":"55 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：