The cover image is based on the Article Automated quantification of crack length and width in asphalt pavements by Zhe Li et al., https://doi.org/10.1111/mice.13344.
{"title":"Cover Image, Volume 39, Issue 21","authors":"","doi":"10.1111/mice.13360","DOIUrl":"10.1111/mice.13360","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>Automated quantification of crack length and width in asphalt pavements</i> by Zhe Li et al., https://doi.org/10.1111/mice.13344.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 21","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liam Cronin, Debarshi Sen, Giulia Marasco, Thomas Matarazzo, Shamim Pakzad
Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time-synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time-synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab-scale experiment for long-span bridges. In the lab-scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross-spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.
{"title":"Bridge monitoring using mobile sensing data with traditional system identification techniques","authors":"Liam Cronin, Debarshi Sen, Giulia Marasco, Thomas Matarazzo, Shamim Pakzad","doi":"10.1111/mice.13358","DOIUrl":"https://doi.org/10.1111/mice.13358","url":null,"abstract":"Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time-synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time-synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab-scale experiment for long-span bridges. In the lab-scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross-spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"75 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bolt loosening assessment is crucial to identify early warnings of structural degradation and prevent catastrophic events. This paper proposes an automatic bolt loosening assessment methodology. First, a novel end‐to‐end ensemble vision model, Bolt‐FP‐Net, is proposed to reason the locations of bolts and their hexagonal feature patterns concurrently. Second, an adaptive target‐free perspective correction method is proposed to correct perspective distortion and enhance assessment accuracy. Finally, an iterative bolt loosening quantification is developed to estimate and refine the bolt loosening rotation. Experimental parametric studies indicated that the proposed Bolt‐FP‐Net can achieve excellent performance under different environmental conditions. Finally, a case study was conducted on steel bolt connections, which shows the proposed methodology can achieve high accuracy and real‐time speed in bolt loosening assessment.
{"title":"Bolt loosening assessment using ensemble vision models for automatic localization and feature extraction with target‐free perspective adaptation","authors":"Xiao Pan, T. Y. Yang","doi":"10.1111/mice.13355","DOIUrl":"https://doi.org/10.1111/mice.13355","url":null,"abstract":"Bolt loosening assessment is crucial to identify early warnings of structural degradation and prevent catastrophic events. This paper proposes an automatic bolt loosening assessment methodology. First, a novel end‐to‐end ensemble vision model, Bolt‐FP‐Net, is proposed to reason the locations of bolts and their hexagonal feature patterns concurrently. Second, an adaptive target‐free perspective correction method is proposed to correct perspective distortion and enhance assessment accuracy. Finally, an iterative bolt loosening quantification is developed to estimate and refine the bolt loosening rotation. Experimental parametric studies indicated that the proposed Bolt‐FP‐Net can achieve excellent performance under different environmental conditions. Finally, a case study was conducted on steel bolt connections, which shows the proposed methodology can achieve high accuracy and real‐time speed in bolt loosening assessment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"16 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhixian Tang, Ruoheng Wang, Edward Chung, Weihua Gu, Hong Zhu
Surrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.
{"title":"An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization","authors":"Zhixian Tang, Ruoheng Wang, Edward Chung, Weihua Gu, Hong Zhu","doi":"10.1111/mice.13354","DOIUrl":"https://doi.org/10.1111/mice.13354","url":null,"abstract":"Surrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"228 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.
{"title":"Natural language processing‐based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete","authors":"Pei‐Fu Zhang, Daxu Zhang, Xiao‐Ling Zhao, Xuan Zhao, Mudassir Iqbal, Yiliyaer Tuerxunmaimaiti, Qi Zhao","doi":"10.1111/mice.13357","DOIUrl":"https://doi.org/10.1111/mice.13357","url":null,"abstract":"As conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"9 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.
{"title":"Efficient 3D robotic mapping and navigation method in complex construction environments","authors":"Tianyu Ren, Houtan Jebelli","doi":"10.1111/mice.13353","DOIUrl":"https://doi.org/10.1111/mice.13353","url":null,"abstract":"Recent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"25 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Offshore earthquakes and subsequent tsunamis pose significant risks to many coastal populations worldwide. This paper introduces a data‐driven machine learning model that synthesizes accurate 3D earthquake–tsunami fragility surfaces from randomly selected 2D fragility curves. The integration of physics‐based simulations enhances the model's reliability for these specific hazards, making it a valuable tool for multi‐hazard analysis in earthquake–tsunami contexts. Additionally, by shifting 2D fragility curves to represent retrofitted structural systems, the model can generate earthquake–tsunami fragility surfaces for community‐level mitigation studies. While the model is demonstrated for earthquake–tsunami scenarios, its methodology architecture has the potential to contribute to other multi‐hazard situations for the initial conditions in multi‐hazard community resilience analysis.
{"title":"Data‐driven machine learning for multi‐hazard fragility surfaces in seismic resilience analysis","authors":"Mojtaba Harati, John W. van de Lindt","doi":"10.1111/mice.13356","DOIUrl":"https://doi.org/10.1111/mice.13356","url":null,"abstract":"Offshore earthquakes and subsequent tsunamis pose significant risks to many coastal populations worldwide. This paper introduces a data‐driven machine learning model that synthesizes accurate 3D earthquake–tsunami fragility surfaces from randomly selected 2D fragility curves. The integration of physics‐based simulations enhances the model's reliability for these specific hazards, making it a valuable tool for multi‐hazard analysis in earthquake–tsunami contexts. Additionally, by shifting 2D fragility curves to represent retrofitted structural systems, the model can generate earthquake–tsunami fragility surfaces for community‐level mitigation studies. While the model is demonstrated for earthquake–tsunami scenarios, its methodology architecture has the potential to contribute to other multi‐hazard situations for the initial conditions in multi‐hazard community resilience analysis.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"53 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Mirzahossein, P. Najafi, N. Kalantari, T. Waller
This paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.
{"title":"Solving discrete network design problem using disjunctive constraints","authors":"H. Mirzahossein, P. Najafi, N. Kalantari, T. Waller","doi":"10.1111/mice.13352","DOIUrl":"https://doi.org/10.1111/mice.13352","url":null,"abstract":"This paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"53 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ensuring the safety of water networks is a research hotspot in the current water conservancy industry, and dams are an important part. However, over time, the dam is prone to varying degrees of aging and disease, most of which are structural cracks. If they cannot be discovered and repaired in time, the normal operation of the dam will be affected, and even catastrophic accidents such as dam failure will occur. However, complex backgrounds and blurred images can easily lead to misjudgments by machine vision detection models, and high-efficiency and accurate detection and evaluation technology are urgently needed. This paper combines the deep semantic segmentation network and the model hyperparameters optimization algorithm to propose a data-intelligent perception method of dam underwater cracks driven by knowledge coupling. Taking the underwater detection of a concrete face rockfill dam as an example, the effectiveness of the model is verified by using the underwater vehicle as the carrier. Experimental results indicate that the developed method achieves an intersection-union ratio of 0.9301, a precision rate of 0.9678, a precision rate of 0.9472, and a recall rate of 0.9577 in the test set. This shows that the constructed method has a high crack fine detection performance. In addition, the developed method has better segmentation performance in different complex underwater crack scenes, which further illustrates the high performance of the developed method.
{"title":"A machine vision-based intelligent segmentation method for dam underwater cracks using swarm optimization algorithm and deep learning","authors":"Yantao Zhu, Xinqiang Niu, Jinzhang Tian","doi":"10.1111/mice.13343","DOIUrl":"https://doi.org/10.1111/mice.13343","url":null,"abstract":"Ensuring the safety of water networks is a research hotspot in the current water conservancy industry, and dams are an important part. However, over time, the dam is prone to varying degrees of aging and disease, most of which are structural cracks. If they cannot be discovered and repaired in time, the normal operation of the dam will be affected, and even catastrophic accidents such as dam failure will occur. However, complex backgrounds and blurred images can easily lead to misjudgments by machine vision detection models, and high-efficiency and accurate detection and evaluation technology are urgently needed. This paper combines the deep semantic segmentation network and the model hyperparameters optimization algorithm to propose a data-intelligent perception method of dam underwater cracks driven by knowledge coupling. Taking the underwater detection of a concrete face rockfill dam as an example, the effectiveness of the model is verified by using the underwater vehicle as the carrier. Experimental results indicate that the developed method achieves an intersection-union ratio of 0.9301, a precision rate of 0.9678, a precision rate of 0.9472, and a recall rate of 0.9577 in the test set. This shows that the constructed method has a high crack fine detection performance. In addition, the developed method has better segmentation performance in different complex underwater crack scenes, which further illustrates the high performance of the developed method.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"10 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant changes in vibration patterns, indicating an approaching train.
{"title":"Prediction of approaching trains based on H‐ranks of track vibration signals","authors":"Ugne Orinaite, Rafal Burdzik, Vinayak Ranjan, Minvydas Ragulskis","doi":"10.1111/mice.13349","DOIUrl":"https://doi.org/10.1111/mice.13349","url":null,"abstract":"This paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant changes in vibration patterns, indicating an approaching train.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"20 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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