Mikiko Yamashita, Koichi Kawanishi, Kenji Hashizume, Pang-jo Chun
Deterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long-term durability. However, traditional methods require core sampling, which often delays detection until damage is extensive. This study proposes a nondestructive approach combining infrared thermography (IRT) and laser-based surface profiling to improve early detection of subsurface damage. IRT captures temperature variations on the pavement surface, detecting horizontal voids and moisture, while laser profiling refines the detection of deeper, progressive damage. By integrating these two methods, the technique offers a comprehensive assessment that single-method approaches cannot provide. Field validation demonstrates that this method enables precise evaluation of bridge deck conditions, contributing to safer and more efficient bridge maintenance.
{"title":"Infrared thermography and 3D pavement surface unevenness measurement algorithm for damage assessment of concrete bridge decks","authors":"Mikiko Yamashita, Koichi Kawanishi, Kenji Hashizume, Pang-jo Chun","doi":"10.1111/mice.13406","DOIUrl":"https://doi.org/10.1111/mice.13406","url":null,"abstract":"Deterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long-term durability. However, traditional methods require core sampling, which often delays detection until damage is extensive. This study proposes a nondestructive approach combining infrared thermography (IRT) and laser-based surface profiling to improve early detection of subsurface damage. IRT captures temperature variations on the pavement surface, detecting horizontal voids and moisture, while laser profiling refines the detection of deeper, progressive damage. By integrating these two methods, the technique offers a comprehensive assessment that single-method approaches cannot provide. Field validation demonstrates that this method enables precise evaluation of bridge deck conditions, contributing to safer and more efficient bridge maintenance.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"57 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884771","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}
The cover image is based on the article Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples by Ranting Cui et al., https://doi.org/10.1111/mice.13242.
封面图像基于Ranting Cui et al., https://doi.org/10.1111/mice.13242基于深度神经网络的时频分解的合成样本结构地震反应训练。
{"title":"Cover Image, Volume 40, Issue 1","authors":"","doi":"10.1111/mice.13404","DOIUrl":"10.1111/mice.13404","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples</i> by Ranting Cui et al., https://doi.org/10.1111/mice.13242.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 1","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874203","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}
Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.
{"title":"Research on autonomous path planning and tracking control methods for unmanned electric shovels","authors":"Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang","doi":"10.1111/mice.13402","DOIUrl":"https://doi.org/10.1111/mice.13402","url":null,"abstract":"Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867106","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}
Accurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal correlation features is proposed. Considering the multidimensional deformation at measurement points, correlations among various points are analyzed through improved self-organizing map clustering and federated Kalman filtering. Second, a temporal convolutional network is employed for improved lower and upper bound estimation, and a quality-driven loss function is adopted to optimize model parameters. Finally, engineering case studies demonstrate that this model can generate reliable prediction intervals for regional deformation, thereby aiding in risk analysis and diagnostics.
{"title":"Uncertainty-informed regional deformation diagnosis of arch dams","authors":"Xudong Chen, Wenhao Sun, Shaowei Hu, Liuyang Li, Chongshi Gu, Jinjun Guo, Bowen Wei, Bo Xu","doi":"10.1111/mice.13395","DOIUrl":"https://doi.org/10.1111/mice.13395","url":null,"abstract":"Accurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal correlation features is proposed. Considering the multidimensional deformation at measurement points, correlations among various points are analyzed through improved self-organizing map clustering and federated Kalman filtering. Second, a temporal convolutional network is employed for improved lower and upper bound estimation, and a quality-driven loss function is adopted to optimize model parameters. Finally, engineering case studies demonstrate that this model can generate reliable prediction intervals for regional deformation, thereby aiding in risk analysis and diagnostics.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"30 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857563","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}
Weihao Sun, Shitong Hou, Gang Wu, Yujie Zhang, Luchang Zhao
Underwater defects in piers pose potential hazards to the safety and durability of river-crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time-consuming. This study proposes a two-step rapid inspection method for underwater concrete bridge piers by combining acoustics and optics. The first step combines macroscopic sonar scanning with an enhanced YOLOv7 to detect and locate piers and defects. Second, the camera approaches the defects for image acquisition, and an enhanced DeepLabv3+ is used for defect identification. The results demonstrate an average mean average precision@0.5 of 95.10% for defect and pier detection, and an mean intersection over union of 0.914 for exposed reinforcement and spalling identification. The method was applied to a real river-crossing bridge and reduced inspection time by 51.2% compared to traditional methods for assessing a row of 11 piers.
{"title":"Two-step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning","authors":"Weihao Sun, Shitong Hou, Gang Wu, Yujie Zhang, Luchang Zhao","doi":"10.1111/mice.13401","DOIUrl":"https://doi.org/10.1111/mice.13401","url":null,"abstract":"Underwater defects in piers pose potential hazards to the safety and durability of river-crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time-consuming. This study proposes a two-step rapid inspection method for underwater concrete bridge piers by combining acoustics and optics. The first step combines macroscopic sonar scanning with an enhanced YOLOv7 to detect and locate piers and defects. Second, the camera approaches the defects for image acquisition, and an enhanced DeepLabv3+ is used for defect identification. The results demonstrate an average mean average precision@0.5 of 95.10% for defect and pier detection, and an mean intersection over union of 0.914 for exposed reinforcement and spalling identification. The method was applied to a real river-crossing bridge and reduced inspection time by 51.2% compared to traditional methods for assessing a row of 11 piers.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"17 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832907","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}
In structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time-intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar geometric shapes. This study introduces a semi-supervised wall segmentation approach (SWS), specifically designed to perform effectively with limited labeled data. SWS combines a deep semantic feature extraction framework with a hierarchical vision transformer and multi-scale feature aggregation to refine feature maps and maintain the spatial precision necessary for pixel-wise segmentation. SWS incorporates consistency regularization to encourage consistent predictions across weak and strong augmentations of the same image. The proposed method improves an intersection over union by more than 4%.
{"title":"A semi-supervised approach for building wall layout segmentation based on transformers and limited data","authors":"Hao Xie, Xiao Ma, Qipei Mei, Ying Hei Chui","doi":"10.1111/mice.13397","DOIUrl":"https://doi.org/10.1111/mice.13397","url":null,"abstract":"In structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time-intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar geometric shapes. This study introduces a semi-supervised wall segmentation approach (SWS), specifically designed to perform effectively with limited labeled data. SWS combines a deep semantic feature extraction framework with a hierarchical vision transformer and multi-scale feature aggregation to refine feature maps and maintain the spatial precision necessary for pixel-wise segmentation. SWS incorporates consistency regularization to encourage consistent predictions across weak and strong augmentations of the same image. The proposed method improves an intersection over union by more than 4%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"200 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821003","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}
Construction robots are challenging the paradigm of labor-intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high-quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed. First, traditional control using controllers has been replaced with vision-based hand gesture control for intuitive demonstration collection. Second, a novel method that integrates both demonstrations and simple environmental rewards is proposed to strike a balance between imitation and exploration. To achieve this goal, a two-step training process is proposed. In the first step, an intuitive demonstration collection platform using virtual reality is utilized. Second, a learning algorithm is used to train a policy for construction tasks. Experimental results demonstrate that combining IL with environmental rewards can significantly accelerate the training, even with limited demonstration data.
{"title":"Training of construction robots using imitation learning and environmental rewards","authors":"Kangkang Duan, Zhengbo Zou, T. Y. Yang","doi":"10.1111/mice.13394","DOIUrl":"https://doi.org/10.1111/mice.13394","url":null,"abstract":"Construction robots are challenging the paradigm of labor-intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high-quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed. First, traditional control using controllers has been replaced with vision-based hand gesture control for intuitive demonstration collection. Second, a novel method that integrates both demonstrations and simple environmental rewards is proposed to strike a balance between imitation and exploration. To achieve this goal, a two-step training process is proposed. In the first step, an intuitive demonstration collection platform using virtual reality is utilized. Second, a learning algorithm is used to train a policy for construction tasks. Experimental results demonstrate that combining IL with environmental rewards can significantly accelerate the training, even with limited demonstration data.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"4 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816372","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}
In the realm of using acoustic methods for locating leakages in water supply pipelines, existing research predominantly focuses on single leak localization, with limited exploration into the challenges posed by multiple leak scenarios. To address this gap, a genetic algorithm‐optimized frequency‐domain convolutional blind source separation algorithm is proposed for the precise localization of multiple leaks. This algorithm effectively separates mixed leak sources and accurately estimates the delays of source propagation. Signal simulations confirm the algorithm's effectiveness, revealing that the distribution of leak positions, signal‐to‐noise ratio, and frequency characteristics of the leakage source all influence the algorithm's performance. Comparative analysis demonstrates the algorithm's capability to eliminate signal interactions, facilitating the localization of multiple leaks. The algorithm's efficacy is further validated through extensive full‐scale experiments, underscoring its potential as a novel and practical solution to the complex challenge of multiple leakage localization in water supply pipelines.
{"title":"Genetic algorithm optimized frequency‐domain convolutional blind source separation for multiple leakage locations in water supply pipeline","authors":"Hongjin Liu, Hongyuan Fang, Xiang Yu, Yangyang Xia","doi":"10.1111/mice.13392","DOIUrl":"https://doi.org/10.1111/mice.13392","url":null,"abstract":"In the realm of using acoustic methods for locating leakages in water supply pipelines, existing research predominantly focuses on single leak localization, with limited exploration into the challenges posed by multiple leak scenarios. To address this gap, a genetic algorithm‐optimized frequency‐domain convolutional blind source separation algorithm is proposed for the precise localization of multiple leaks. This algorithm effectively separates mixed leak sources and accurately estimates the delays of source propagation. Signal simulations confirm the algorithm's effectiveness, revealing that the distribution of leak positions, signal‐to‐noise ratio, and frequency characteristics of the leakage source all influence the algorithm's performance. Comparative analysis demonstrates the algorithm's capability to eliminate signal interactions, facilitating the localization of multiple leaks. The algorithm's efficacy is further validated through extensive full‐scale experiments, underscoring its potential as a novel and practical solution to the complex challenge of multiple leakage localization in water supply pipelines.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"119 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815749","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}
Zihang Weng, Chenglong Liu, Yuchuan Du, Difei Wu, Zhen Leng
The pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression R2 of 0.8470, which was a 20.25% improvement over the baseline model.
{"title":"Integrating spatial and channel attention mechanisms with domain knowledge in convolutional neural networks for friction coefficient prediction","authors":"Zihang Weng, Chenglong Liu, Yuchuan Du, Difei Wu, Zhen Leng","doi":"10.1111/mice.13391","DOIUrl":"https://doi.org/10.1111/mice.13391","url":null,"abstract":"The pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction mechanisms and deep neural networks has not been fully studied. This study employed spatial‐channel attention mechanisms to integrate frictional domain knowledge and convolutional neural networks (CNNs) that predict the friction coefficient as the output. The models’ inputs include 3D texture data, corresponding finite element (FE) simulation outcomes, and 2D wavelet decomposition outcomes. An additional spatial attention (ASA) mechanism guided the CNNs to focus on the tire–road contact region, using tire–road contact stress from FE simulation as domain knowledge. Multi‐scale channel attention (MSCA) mechanisms enabled the CNNs to learn the channel weights of 2D‐wavelet‐based multi‐scale inputs, thereby assessing the contribution of different texture scales to tire–road friction. A multi‐attention and feature fusion mechanism was designed, and the performances of various combinations were compared. The results showed that the fusion of ASA and MSCA achieved the best performance, with a regression <jats:italic>R</jats:italic><jats:sup>2</jats:sup> of 0.8470, which was a 20.25% improvement over the baseline model.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"36 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797166","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}
Rock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT) algorithm is employed for rapid image calibration. A K‐Net‐based model with dynamic semantic kernels, conditional on their actual activations, is proposed for rock core segmentation. It surpasses other prevalent models with a mean intersection over union of 95.43%. The automatic RQD estimation error of our proposed framework is only 1.46% compared to manual logging results, demonstrating its exceptional reliability and effectiveness. The robustness of the framework is then validated on an additional test set, proving its potential for widespread adoption in geotechnical engineering practice.
{"title":"A K‐Net‐based deep learning framework for automatic rock quality designation estimation","authors":"Sihao Yu, Louis Ngai Yuen Wong","doi":"10.1111/mice.13386","DOIUrl":"https://doi.org/10.1111/mice.13386","url":null,"abstract":"Rock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT) algorithm is employed for rapid image calibration. A K‐Net‐based model with dynamic semantic kernels, conditional on their actual activations, is proposed for rock core segmentation. It surpasses other prevalent models with a mean intersection over union of 95.43%. The automatic RQD estimation error of our proposed framework is only 1.46% compared to manual logging results, demonstrating its exceptional reliability and effectiveness. The robustness of the framework is then validated on an additional test set, proving its potential for widespread adoption in geotechnical engineering practice.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"95 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797167","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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