Pub Date : 2022-02-27DOI: 10.1080/23789689.2022.2028595
A. Jafari, E. Rajabi, Gholamreza Ghodrati Amiri, S. A. R. Amrei
ABSTRACT In this paper, seismic hazard curves are selected for four different sites at Alborz and Zagros seismic zones with underlying conditions of soft and hard soil. Three residential steel moment-resisting frames (SMRFs) with 3, 5, and 7 stories are designed and modeled for each site to perform cloud analysis, implementing more than 300 far-field as-recorded ground motions. Using the results of regression-based cloud analysis, robust fragility curves are developed for each SMRF, and by combining the fragility curve with the hazard curve, the annual rate of collapse (i.e., collapse risk) is calculated for each SMRF. Furthermore, the important levels of peak ground acceleration (PGA) that greatly contribute to the collapse risk of each SMRF are obtained by exploiting the collapse deaggregation curves, which are composed up of fragility curves, derivative of hazard curves, and the annual rates of collapse. It was found that a strong correlation exists between ductility and the collapse risk of SMRFs at both seismic zones and underlying soil conditions, where increasing the ductility of SMRFs results in a decreased risk of collapse. It was also found that the important levels of the PGA contributing to the collapse risk of the SMRFs are inclined towards greater values as the ductility increases. The 5-storey SMRFs exhibited the least ductility and the highest collapse risk at both seismic zones and soil conditions, while the 3-storey SMRFs were the most ductile ones with the least risk of collapse.
{"title":"The effect of ductility on the seismic collapse risk of residential steel moment-resisting frames at Alborz and Zagros Seismic zones, Iran","authors":"A. Jafari, E. Rajabi, Gholamreza Ghodrati Amiri, S. A. R. Amrei","doi":"10.1080/23789689.2022.2028595","DOIUrl":"https://doi.org/10.1080/23789689.2022.2028595","url":null,"abstract":"ABSTRACT In this paper, seismic hazard curves are selected for four different sites at Alborz and Zagros seismic zones with underlying conditions of soft and hard soil. Three residential steel moment-resisting frames (SMRFs) with 3, 5, and 7 stories are designed and modeled for each site to perform cloud analysis, implementing more than 300 far-field as-recorded ground motions. Using the results of regression-based cloud analysis, robust fragility curves are developed for each SMRF, and by combining the fragility curve with the hazard curve, the annual rate of collapse (i.e., collapse risk) is calculated for each SMRF. Furthermore, the important levels of peak ground acceleration (PGA) that greatly contribute to the collapse risk of each SMRF are obtained by exploiting the collapse deaggregation curves, which are composed up of fragility curves, derivative of hazard curves, and the annual rates of collapse. It was found that a strong correlation exists between ductility and the collapse risk of SMRFs at both seismic zones and underlying soil conditions, where increasing the ductility of SMRFs results in a decreased risk of collapse. It was also found that the important levels of the PGA contributing to the collapse risk of the SMRFs are inclined towards greater values as the ductility increases. The 5-storey SMRFs exhibited the least ductility and the highest collapse risk at both seismic zones and soil conditions, while the 3-storey SMRFs were the most ductile ones with the least risk of collapse.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"7 1","pages":"715 - 743"},"PeriodicalIF":5.9,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42590723","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 : 2022-02-23DOI: 10.1080/23789689.2022.2029324
Noam Rosenthal, M. Chester, Andrew M. Fraser, D. Hondula, D. Eisenman
ABSTRACT Extreme heat events induced by climate change present a growing risk to transit passenger comfort and health. To reduce exposure, agencies may consider changes to schedules that reduce headways on heavily trafficked bus routes serving vulnerable populations. This paper develops a schedule optimization model to minimize heat exposure and applies it to local bus services in Phoenix, Arizona, using agent-based simulation to inform travel demand and rider characteristics. Rerouting as little as 10% of a fleet is found to reduce network-wide exposure by as much as 35% when operating at maximum fleet capacity. Outcome improvements are notably characterized by diminishing returns, owing to skewed ridership and the inverse relationship between fleet size and passenger wait time. Access to spare vehicles can also ensure significant reductions in exposure, especially under the most extreme temperatures. Rerouting, therefore, presents a low-cost, adaptable resilience strategy to protect riders from extreme heat exposure.
{"title":"Adaptive transit scheduling to reduce rider vulnerability during heatwaves","authors":"Noam Rosenthal, M. Chester, Andrew M. Fraser, D. Hondula, D. Eisenman","doi":"10.1080/23789689.2022.2029324","DOIUrl":"https://doi.org/10.1080/23789689.2022.2029324","url":null,"abstract":"ABSTRACT Extreme heat events induced by climate change present a growing risk to transit passenger comfort and health. To reduce exposure, agencies may consider changes to schedules that reduce headways on heavily trafficked bus routes serving vulnerable populations. This paper develops a schedule optimization model to minimize heat exposure and applies it to local bus services in Phoenix, Arizona, using agent-based simulation to inform travel demand and rider characteristics. Rerouting as little as 10% of a fleet is found to reduce network-wide exposure by as much as 35% when operating at maximum fleet capacity. Outcome improvements are notably characterized by diminishing returns, owing to skewed ridership and the inverse relationship between fleet size and passenger wait time. Access to spare vehicles can also ensure significant reductions in exposure, especially under the most extreme temperatures. Rerouting, therefore, presents a low-cost, adaptable resilience strategy to protect riders from extreme heat exposure.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"7 1","pages":"744 - 755"},"PeriodicalIF":5.9,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47496191","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 : 2022-02-22DOI: 10.1080/23789689.2021.2017736
Moeid Shariatfar, Yong-Cheol Lee, Kunhee Choi, Minkyum Kim
ABSTRACT Catastrophic flood and hurricane events cause enormous adversarial impacts on roadways and their long-term performance. Despite the abundance of research attempting to understand the flood-induced damages, a critical knowledge gap in assessing roadway flood damage at a network-level exists. This study develops a holistic assessment model that evaluates network-level flood damage of roadways based on historic pavement distress data along with historic flood data. A rich volume of high-confidence historical pavement distress data was obtained from the Louisiana pavement management system. After a rigorous data pre-processing process by cross-referencing the flooded areas using the 2016 Louisiana flood map data, it was leveraged to analyze how flooding could interact with the pavement distress, thus affecting the overall performance of existing pavements. The study outcomes showed that the most flood-affected distress types include roughness and random cracking. Based on the findings from the analysis, this study developed a machine learning-based prediction method that can calculate future pavement performance after a flood event. After applying different algorithms for creating the prediction model, the eXtreme Gradient Boosting (XGB) classifier was selected because it represented the highest accuracy among other examined classifiers. Various datasets and scenarios were investigated with the developed prediction model to identify the most effective features and dataset combinations. The prediction model is expected to identify vulnerable pavement sections and facilitate network-level preventive maintenance of pavement to mitigate future flooding impacts by prioritizing resource allocations for maintenance and rehabilitation after a flood event.
{"title":"Effects of flooding on pavement performance: a machine learning-based network-level assessment","authors":"Moeid Shariatfar, Yong-Cheol Lee, Kunhee Choi, Minkyum Kim","doi":"10.1080/23789689.2021.2017736","DOIUrl":"https://doi.org/10.1080/23789689.2021.2017736","url":null,"abstract":"ABSTRACT Catastrophic flood and hurricane events cause enormous adversarial impacts on roadways and their long-term performance. Despite the abundance of research attempting to understand the flood-induced damages, a critical knowledge gap in assessing roadway flood damage at a network-level exists. This study develops a holistic assessment model that evaluates network-level flood damage of roadways based on historic pavement distress data along with historic flood data. A rich volume of high-confidence historical pavement distress data was obtained from the Louisiana pavement management system. After a rigorous data pre-processing process by cross-referencing the flooded areas using the 2016 Louisiana flood map data, it was leveraged to analyze how flooding could interact with the pavement distress, thus affecting the overall performance of existing pavements. The study outcomes showed that the most flood-affected distress types include roughness and random cracking. Based on the findings from the analysis, this study developed a machine learning-based prediction method that can calculate future pavement performance after a flood event. After applying different algorithms for creating the prediction model, the eXtreme Gradient Boosting (XGB) classifier was selected because it represented the highest accuracy among other examined classifiers. Various datasets and scenarios were investigated with the developed prediction model to identify the most effective features and dataset combinations. The prediction model is expected to identify vulnerable pavement sections and facilitate network-level preventive maintenance of pavement to mitigate future flooding impacts by prioritizing resource allocations for maintenance and rehabilitation after a flood event.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"7 1","pages":"695 - 714"},"PeriodicalIF":5.9,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42192776","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 : 2022-02-12DOI: 10.1080/23789689.2021.2004646
S. Esposito, A. Botta, M. De Falco, Adriana Pacifico, E. Chioccarelli, A. Pescapé, A. Santo, I. Iervolino
ABSTRACT Data communication networks have large importance for the immediate post-earthquake emergency management and community resilience. In this study, the framework of simulation-based probabilistic seismic risk analysis of data communication infrastructure is applied to the real case of the inter-university data network of the Campania region (southern Italy). The network is constituted by point-like facilities (racks located within buildings and containing the device routing and managing traffic) and distributed links (buried fiber optic cables). The seismological, geological, and geotechnical features of the region were characterized together with the seismic vulnerability of each element of the network. The network performance is quantified in terms of traffic loss before and after the seismic event. Results are provided in terms of annual rate of events exceeding traffic loss thresholds and allow to identify the portion of the network mostly contributing to the seismic performance.
{"title":"Seismic risk analysis of a data communication network","authors":"S. Esposito, A. Botta, M. De Falco, Adriana Pacifico, E. Chioccarelli, A. Pescapé, A. Santo, I. Iervolino","doi":"10.1080/23789689.2021.2004646","DOIUrl":"https://doi.org/10.1080/23789689.2021.2004646","url":null,"abstract":"ABSTRACT Data communication networks have large importance for the immediate post-earthquake emergency management and community resilience. In this study, the framework of simulation-based probabilistic seismic risk analysis of data communication infrastructure is applied to the real case of the inter-university data network of the Campania region (southern Italy). The network is constituted by point-like facilities (racks located within buildings and containing the device routing and managing traffic) and distributed links (buried fiber optic cables). The seismological, geological, and geotechnical features of the region were characterized together with the seismic vulnerability of each element of the network. The network performance is quantified in terms of traffic loss before and after the seismic event. Results are provided in terms of annual rate of events exceeding traffic loss thresholds and allow to identify the portion of the network mostly contributing to the seismic performance.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"7 1","pages":"655 - 672"},"PeriodicalIF":5.9,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46780801","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 : 2022-01-30DOI: 10.1080/23789689.2021.2000146
L. Iannacone, P. Gardoni
ABSTRACT Current approaches to assess the reliability and resilience of water infrastructure subject to seismic hazard typically use Repair Rate (RR) curves for the linear elements (pipelines), which estimate the expected number of repairs needed per unit length after the occurrence of an earthquake of a given intensity. The available RR curves are characterized by high levels of uncertainty being based primarily on expert judgment and on limited data. Also, they provide no distinction between the damage on the segments and on the joints. This paper develops probabilistic physics-based RR curves to quantify the damage to segmented pipelines due to earthquakes. First, a mechanical model for segmented pipelines is proposed. The model is then used to generate a set of synthetic data for the calibration of the model parameters. We compare the proposed RR curves with the ones available in the literature and discuss the advantages of the proposed model.
{"title":"Physics-based repair rate curves for segmented pipelines subject to seismic excitations","authors":"L. Iannacone, P. Gardoni","doi":"10.1080/23789689.2021.2000146","DOIUrl":"https://doi.org/10.1080/23789689.2021.2000146","url":null,"abstract":"ABSTRACT Current approaches to assess the reliability and resilience of water infrastructure subject to seismic hazard typically use Repair Rate (RR) curves for the linear elements (pipelines), which estimate the expected number of repairs needed per unit length after the occurrence of an earthquake of a given intensity. The available RR curves are characterized by high levels of uncertainty being based primarily on expert judgment and on limited data. Also, they provide no distinction between the damage on the segments and on the joints. This paper develops probabilistic physics-based RR curves to quantify the damage to segmented pipelines due to earthquakes. First, a mechanical model for segmented pipelines is proposed. The model is then used to generate a set of synthetic data for the calibration of the model parameters. We compare the proposed RR curves with the ones available in the literature and discuss the advantages of the proposed model.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"8 1","pages":"121 - 141"},"PeriodicalIF":5.9,"publicationDate":"2022-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42577801","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 : 2021-12-20DOI: 10.1080/23789689.2021.1996811
E. Porse, C. Poindexter, Christian Carelton, M. Stephens
ABSTRACT Extreme precipitation from climate change may strain many existing stormwater systems. While studies have evaluated such effects on stormwater infrastructure, other sources of uncertainty not yet explored should also be considered. This paper presents an analysis of adaptation costs for new stormwater infrastructure to mitigate increases in design storm precipitation depth with climate change, including how economic and managerial uncertainty related to life cycle unit costs and knowledge of existing infrastructure affect costs. For case study areas in California, we quantify adaptation costs for new green infrastructure capacity by evaluating future design storms. Results indicate that design storm depths increase by an average of 28%, but lack of knowledge of the condition of existing infrastructure and life cycle unit costs result in wide cost ranges. The findings illustrate how climate change planning for stormwater should also consider economic and managerial uncertainty when estimating long-term adaptation costs.
{"title":"Climate change risk and adaptation costs for stormwater management in California coastal parklands","authors":"E. Porse, C. Poindexter, Christian Carelton, M. Stephens","doi":"10.1080/23789689.2021.1996811","DOIUrl":"https://doi.org/10.1080/23789689.2021.1996811","url":null,"abstract":"ABSTRACT Extreme precipitation from climate change may strain many existing stormwater systems. While studies have evaluated such effects on stormwater infrastructure, other sources of uncertainty not yet explored should also be considered. This paper presents an analysis of adaptation costs for new stormwater infrastructure to mitigate increases in design storm precipitation depth with climate change, including how economic and managerial uncertainty related to life cycle unit costs and knowledge of existing infrastructure affect costs. For case study areas in California, we quantify adaptation costs for new green infrastructure capacity by evaluating future design storms. Results indicate that design storm depths increase by an average of 28%, but lack of knowledge of the condition of existing infrastructure and life cycle unit costs result in wide cost ranges. The findings illustrate how climate change planning for stormwater should also consider economic and managerial uncertainty when estimating long-term adaptation costs.","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"8 1","pages":"293 - 306"},"PeriodicalIF":5.9,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43302083","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 : 2021-10-29DOI: 10.1007/978-981-16-6978-1_38
N. Zahari, M. Zawawi, Fei Chong Ng, L. Sidek, A. Abas, F. Nurhikmah, N. A. Aziz, Tung Lun Hao, M. Radzi
{"title":"Particle Image Velocimetry Dynamic Analysis on the Penstock Vortex Flow for the Dam Reliability Study","authors":"N. Zahari, M. Zawawi, Fei Chong Ng, L. Sidek, A. Abas, F. Nurhikmah, N. A. Aziz, Tung Lun Hao, M. Radzi","doi":"10.1007/978-981-16-6978-1_38","DOIUrl":"https://doi.org/10.1007/978-981-16-6978-1_38","url":null,"abstract":"","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"13 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87343746","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 : 2021-10-29DOI: 10.1007/978-981-16-6978-1_6
Ziya Sameer Mohamed, S. Dash
{"title":"An Explorative Study on Material Feasibility for Relief Shelter for Refugees","authors":"Ziya Sameer Mohamed, S. Dash","doi":"10.1007/978-981-16-6978-1_6","DOIUrl":"https://doi.org/10.1007/978-981-16-6978-1_6","url":null,"abstract":"","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"2 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81960025","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 : 2021-10-29DOI: 10.1007/978-981-16-6978-1_30
Jessada Sresakoolchai, S. Kaewunruen
{"title":"Integration of Building Information Modeling (BIM) and Artificial Intelligence (AI) to Detect Combined Defects of Infrastructure in the Railway System","authors":"Jessada Sresakoolchai, S. Kaewunruen","doi":"10.1007/978-981-16-6978-1_30","DOIUrl":"https://doi.org/10.1007/978-981-16-6978-1_30","url":null,"abstract":"","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"20 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78273286","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 : 2021-10-29DOI: 10.1007/978-981-16-6978-1_9
C. Vaidevi, D. Vijayan, C. Nivetha, M. Kalpana
{"title":"COVID-19 Future Proof Infrastructure","authors":"C. Vaidevi, D. Vijayan, C. Nivetha, M. Kalpana","doi":"10.1007/978-981-16-6978-1_9","DOIUrl":"https://doi.org/10.1007/978-981-16-6978-1_9","url":null,"abstract":"","PeriodicalId":45395,"journal":{"name":"Sustainable and Resilient Infrastructure","volume":"24 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75770785","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}