Pub Date : 2022-11-17DOI: 10.1186/s43065-022-00059-0
Phung Tu, V. Vimonsatit, C. Hansapinyo
{"title":"Frequency spectrum of engineering structures with time varying masses","authors":"Phung Tu, V. Vimonsatit, C. Hansapinyo","doi":"10.1186/s43065-022-00059-0","DOIUrl":"https://doi.org/10.1186/s43065-022-00059-0","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"3 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44545466","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 : 2022-10-14DOI: 10.1186/s43065-022-00058-1
Jun Wang, Y. J. Kim
{"title":"A state-of-the-art review of prestressed concrete tub girders for bridge structures","authors":"Jun Wang, Y. J. Kim","doi":"10.1186/s43065-022-00058-1","DOIUrl":"https://doi.org/10.1186/s43065-022-00058-1","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"3 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42397722","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 : 2022-10-10DOI: 10.1186/s43065-022-00057-2
A. Baral, M. Shahandashti
{"title":"Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards","authors":"A. Baral, M. Shahandashti","doi":"10.1186/s43065-022-00057-2","DOIUrl":"https://doi.org/10.1186/s43065-022-00057-2","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"3 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49214037","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 : 2022-09-20DOI: 10.1186/s43065-022-00056-3
Ju-Hyung Kim, Christopher J. Hessek, Y. J. Kim, Hong-Gun Park
{"title":"Seismic analysis, design, and retrofit of built-environments: a procedural review of current practices and case studies","authors":"Ju-Hyung Kim, Christopher J. Hessek, Y. J. Kim, Hong-Gun Park","doi":"10.1186/s43065-022-00056-3","DOIUrl":"https://doi.org/10.1186/s43065-022-00056-3","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46624368","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 : 2022-08-03DOI: 10.1186/s43065-022-00055-4
Zhao, Mengchen, Sadhu, Ayan, Capretz, Miriam
Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%.
{"title":"Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network","authors":"Zhao, Mengchen, Sadhu, Ayan, Capretz, Miriam","doi":"10.1186/s43065-022-00055-4","DOIUrl":"https://doi.org/10.1186/s43065-022-00055-4","url":null,"abstract":"Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"413 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514260","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}
Asphalt pavement is vulnerable to the temperature rising and extremely high-temperature weather caused by climate change. The regulation techniques of asphalt pavement high temperature have become a growing concern to adapt to climate change. This paper reviewed the state of the art on regulating asphalt pavement high temperature. Firstly, the influencing factors and potential regulation paths of asphalt pavement temperature were summarized. The regulation techniques were categorized into two categories. One is to regulate the heat transfer process, including enhancing reflection, increasing thermal resistance, and evaporation cooling. The other is to regulate through heat collection and transfer or conversion, including embedded heat exchange system, phase change asphalt pavement, and thermoelectric system. Then, the regulation techniques in the literature were reviewed one by one in terms of cooling effects and pavement performance. The issues that still need to be improved were also discussed. Finally, the regulation techniques were compared from the perspectives of theoretical cooling effects, construction convenience, and required maintenance. It can provide reference for understanding the development status of asphalt pavement high temperature regulation techniques and technique selection in practice.
{"title":"Review of regulation techniques of asphalt pavement high temperature for climate change adaptation","authors":"Gong, Zhenlong, Zhang, Letao, Wu, Jiaxi, Xiu, Zhao, Wang, Linbing, Miao, Yinghao","doi":"10.1186/s43065-022-00054-5","DOIUrl":"https://doi.org/10.1186/s43065-022-00054-5","url":null,"abstract":"Asphalt pavement is vulnerable to the temperature rising and extremely high-temperature weather caused by climate change. The regulation techniques of asphalt pavement high temperature have become a growing concern to adapt to climate change. This paper reviewed the state of the art on regulating asphalt pavement high temperature. Firstly, the influencing factors and potential regulation paths of asphalt pavement temperature were summarized. The regulation techniques were categorized into two categories. One is to regulate the heat transfer process, including enhancing reflection, increasing thermal resistance, and evaporation cooling. The other is to regulate through heat collection and transfer or conversion, including embedded heat exchange system, phase change asphalt pavement, and thermoelectric system. Then, the regulation techniques in the literature were reviewed one by one in terms of cooling effects and pavement performance. The issues that still need to be improved were also discussed. Finally, the regulation techniques were compared from the perspectives of theoretical cooling effects, construction convenience, and required maintenance. It can provide reference for understanding the development status of asphalt pavement high temperature regulation techniques and technique selection in practice.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514296","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 : 2022-05-21DOI: 10.1186/s43065-022-00053-6
Nasr, Amro, Honfi, Dániel, Larsson Ivanov, Oskar
The impact of climate change on the deterioration of reinforced concrete elements have been frequently highlighted as worthy of investigation. This article addresses this important issue by presenting a time-variant reliability analysis to assess the effect of climate change on four limit states; the probabilities of corrosion initiation, crack initiation, severe cracking, and failure of a simply supported beam built in 2020 and exposed to chloride-induced corrosion. The historical and future climate conditions (as projected by three different emission scenarios) for different climate zones in Sweden are considered, including subarctic conditions where the impact of climate change may lead to large increases in temperature. The probabilities of all limit states are found to be: 1) higher for scenarios with higher GHG emissions and 2) higher for southern than for northern climate zones. However, the end-of-century impact of climate change on the probabilities of reaching the different limit states is found to be higher for northern than for southern climate zones. At 2100, the impact of climate change on the probability of failure can reach up to an increase of 123% for the northernmost zone. It is also noted that the end-of-century impact on the probability of failure is significantly higher (ranging from 3.5–4.9 times higher) than on the other limit states in all climate scenarios.
{"title":"Probabilistic analysis of climate change impact on chloride-induced deterioration of reinforced concrete considering Nordic climate","authors":"Nasr, Amro, Honfi, Dániel, Larsson Ivanov, Oskar","doi":"10.1186/s43065-022-00053-6","DOIUrl":"https://doi.org/10.1186/s43065-022-00053-6","url":null,"abstract":"The impact of climate change on the deterioration of reinforced concrete elements have been frequently highlighted as worthy of investigation. This article addresses this important issue by presenting a time-variant reliability analysis to assess the effect of climate change on four limit states; the probabilities of corrosion initiation, crack initiation, severe cracking, and failure of a simply supported beam built in 2020 and exposed to chloride-induced corrosion. The historical and future climate conditions (as projected by three different emission scenarios) for different climate zones in Sweden are considered, including subarctic conditions where the impact of climate change may lead to large increases in temperature. The probabilities of all limit states are found to be: 1) higher for scenarios with higher GHG emissions and 2) higher for southern than for northern climate zones. However, the end-of-century impact of climate change on the probabilities of reaching the different limit states is found to be higher for northern than for southern climate zones. At 2100, the impact of climate change on the probability of failure can reach up to an increase of 123% for the northernmost zone. It is also noted that the end-of-century impact on the probability of failure is significantly higher (ranging from 3.5–4.9 times higher) than on the other limit states in all climate scenarios.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"193 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514211","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 : 2022-05-17DOI: 10.1186/s43065-022-00052-7
Yu Zhang, Yaohan Li, You Dong
{"title":"Probabilistic analysis of long-term loss incorporating maximum entropy method and analytical higher-order moments","authors":"Yu Zhang, Yaohan Li, You Dong","doi":"10.1186/s43065-022-00052-7","DOIUrl":"https://doi.org/10.1186/s43065-022-00052-7","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65800601","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 : 2022-04-08DOI: 10.1186/s43065-022-00051-8
Barbosh, Mohamed, Dunphy, Kyle, Sadhu, Ayan
Acoustic Emission (AE) has emerged as a popular damage detection and localization tool due to its high performance in identifying minor damage or crack. Due to the high sampling rate, AE sensors result in massive data during long-term monitoring of large-scale civil structures. Analyzing such big data and associated AE parameters (e.g., rise time, amplitude, counts, etc.) becomes time-consuming using traditional feature extraction methods. This paper proposes a 2D convolutional neural network (2D CNN)-based Artificial Intelligence (AI) algorithm combined with time–frequency decomposition techniques to extract the damage information from the measured AE data without using standalone AE parameters. In this paper, Empirical Mode Decomposition (EMD) is employed to extract the intrinsic mode functions (IMFs) from noisy raw AE measurements, where the IMFs serve as the key AE components of the data. Continuous Wavelet Transform (CWT) is then used to obtain the spectrograms of the AE components, serving as the “artificial images” to an AI network. These spectrograms are fed into 2D CNN algorithm to detect and identify the potential location of the damage. The proposed approach is validated using a suite of numerical and experimental studies.
{"title":"Acoustic emission-based damage localization using wavelet-assisted deep learning","authors":"Barbosh, Mohamed, Dunphy, Kyle, Sadhu, Ayan","doi":"10.1186/s43065-022-00051-8","DOIUrl":"https://doi.org/10.1186/s43065-022-00051-8","url":null,"abstract":"Acoustic Emission (AE) has emerged as a popular damage detection and localization tool due to its high performance in identifying minor damage or crack. Due to the high sampling rate, AE sensors result in massive data during long-term monitoring of large-scale civil structures. Analyzing such big data and associated AE parameters (e.g., rise time, amplitude, counts, etc.) becomes time-consuming using traditional feature extraction methods. This paper proposes a 2D convolutional neural network (2D CNN)-based Artificial Intelligence (AI) algorithm combined with time–frequency decomposition techniques to extract the damage information from the measured AE data without using standalone AE parameters. In this paper, Empirical Mode Decomposition (EMD) is employed to extract the intrinsic mode functions (IMFs) from noisy raw AE measurements, where the IMFs serve as the key AE components of the data. Continuous Wavelet Transform (CWT) is then used to obtain the spectrograms of the AE components, serving as the “artificial images” to an AI network. These spectrograms are fed into 2D CNN algorithm to detect and identify the potential location of the damage. The proposed approach is validated using a suite of numerical and experimental studies.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514275","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 : 2022-03-07DOI: 10.1186/s43065-022-00050-9
Fujian Tang, Zhibin Lin, H. Qu, Genda Chen
{"title":"Investigation into corrosion-induced bond degradation between concrete and steel rebar with acoustic emission and 3D laser scan techniques","authors":"Fujian Tang, Zhibin Lin, H. Qu, Genda Chen","doi":"10.1186/s43065-022-00050-9","DOIUrl":"https://doi.org/10.1186/s43065-022-00050-9","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65800579","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}
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