The cover image is based on the article Attention-optimized 3D segmentation and reconstruction system for sewer pipelines employing multi-]view images by Wang Niannian et al., https://doi.org/10.1111/mice.13241.
{"title":"Cover Image, Volume 40, Issue 5","authors":"","doi":"10.1111/mice.13433","DOIUrl":"https://doi.org/10.1111/mice.13433","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Attention-optimized 3D segmentation and reconstruction system for sewer pipelines employing multi-]view images</i> by Wang Niannian et al., https://doi.org/10.1111/mice.13241.\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 5","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111878","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}
Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran
With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to model arterial dynamics and the cell transmission model to capture expressway dynamics. A stochastic user equilibrium model is further established for route choice, and a decision model is developed to minimize total time spent. Simulations show that new expressways alleviate network congestion, with significant effects in the initial stages. Moreover, route guidance strategies and driver compliance also influence the schemes.
{"title":"The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram","authors":"Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran","doi":"10.1111/mice.13435","DOIUrl":"https://doi.org/10.1111/mice.13435","url":null,"abstract":"With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to model arterial dynamics and the cell transmission model to capture expressway dynamics. A stochastic user equilibrium model is further established for route choice, and a decision model is developed to minimize total time spent. Simulations show that new expressways alleviate network congestion, with significant effects in the initial stages. Moreover, route guidance strategies and driver compliance also influence the schemes.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083148","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}
Pierclaudio Savino, Fabio Graglia, Gabriele Scozza, Vincenzo Di Pietra
Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning-based approach for semantic segmentation of corroded areas on steel towers. Using the DeepLabv3+ model, the network was trained and validated on 999 field photographs. MobileNetV2, serving as the feature extractor, was chosen for its optimal balance between accuracy and computational efficiency, achieving a validation accuracy of 90.8% and a loss of 0.23. The trained network was applied to real-world inspections using orthomosaics derived from photogrammetric reconstructions of the South-East tower at the Torino Eremo broadcasting center. These photogrammetric products not only enabled precise segmentation of corroded areas but also provided the foundation for corrosion quantification with metrical accuracy, a critical advantage for maintenance planning. Unlike traditional image segmentation methods, which lack a spatial reference and precise scaling, the photogrammetric approach ensures that the corrosion extent and distribution are quantified in exact physical dimensions, enhancing the reliability of the analysis. The results show that deep learning-based inspections can automate detection, providing reliable data and reducing reliance on manual inspections, enhancing efficiency, safety, and accuracy.
{"title":"Automated corrosion surface quantification in steel transmission towers using UAV photogrammetry and deep convolutional neural networks","authors":"Pierclaudio Savino, Fabio Graglia, Gabriele Scozza, Vincenzo Di Pietra","doi":"10.1111/mice.13434","DOIUrl":"https://doi.org/10.1111/mice.13434","url":null,"abstract":"Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning-based approach for semantic segmentation of corroded areas on steel towers. Using the DeepLabv3+ model, the network was trained and validated on 999 field photographs. MobileNetV2, serving as the feature extractor, was chosen for its optimal balance between accuracy and computational efficiency, achieving a validation accuracy of 90.8% and a loss of 0.23. The trained network was applied to real-world inspections using orthomosaics derived from photogrammetric reconstructions of the South-East tower at the Torino Eremo broadcasting center. These photogrammetric products not only enabled precise segmentation of corroded areas but also provided the foundation for corrosion quantification with metrical accuracy, a critical advantage for maintenance planning. Unlike traditional image segmentation methods, which lack a spatial reference and precise scaling, the photogrammetric approach ensures that the corrosion extent and distribution are quantified in exact physical dimensions, enhancing the reliability of the analysis. The results show that deep learning-based inspections can automate detection, providing reliable data and reducing reliance on manual inspections, enhancing efficiency, safety, and accuracy.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"25 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124406","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}
With the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC-E) algorithm and real-time progress monitoring. SAC-E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning. In addition, robotic kinematics and a control algorithm are implemented to ensure that the crane can autonomously execute the lifting path. Last, novel hardware communication interfaces between robot operating system and building information modeling (BIM) are developed for real-time construction progress monitoring. The performance of the proposed framework was demonstrated using a robotized mobile crane to stack concrete retaining blocks. The results show that the proposed framework can be effectively used to execute the retaining block construction using the robotized mobile crane with real-time construction update in the BIM platform.
{"title":"Autonomous construction framework for crane control with enhanced soft actor–critic algorithm and real-time progress monitoring","authors":"Yifei Xiao, T. Y. Yang, Fan Xie","doi":"10.1111/mice.13427","DOIUrl":"https://doi.org/10.1111/mice.13427","url":null,"abstract":"With the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC-E) algorithm and real-time progress monitoring. SAC-E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning. In addition, robotic kinematics and a control algorithm are implemented to ensure that the crane can autonomously execute the lifting path. Last, novel hardware communication interfaces between robot operating system and building information modeling (BIM) are developed for real-time construction progress monitoring. The performance of the proposed framework was demonstrated using a robotized mobile crane to stack concrete retaining blocks. The results show that the proposed framework can be effectively used to execute the retaining block construction using the robotized mobile crane with real-time construction update in the BIM platform.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"14 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056574","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}
Computer vision-based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic steel deck, stress at the structural details belongs to the typical third-class system, and related research requires accurate wheel load position. Based on the principle of camera imaging, this study proposes an innovative vehicle wheel load location method based on vehicle license plate detection and multiple projective planes, and the accurate positioning of the vehicle center is achieved by the projective relationship matrix of different planes. Then, accurate measurement of the lateral wheelbase is achieved through secondary detection and projective transformation. Further, accurate wheel load tracking for fatigue research is achieved by the multi-objective tracking algorithm. Based on theoretical analysis and practical application results, the effectiveness and accuracy of this method have been verified. Different from traditional positioning methods based on vehicle detection boxes and 3D reconstruction boxes, the proposed method has higher accuracy and will play a fundamental role in the use of vehicle load spatiotemporal data for more accurate analysis such as fatigue research.
{"title":"Vehicle wheel load positioning method based on multiple projective planes","authors":"Kai Sun, Xu Jiang, Xuhong Qiang","doi":"10.1111/mice.13432","DOIUrl":"https://doi.org/10.1111/mice.13432","url":null,"abstract":"Computer vision-based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic steel deck, stress at the structural details belongs to the typical third-class system, and related research requires accurate wheel load position. Based on the principle of camera imaging, this study proposes an innovative vehicle wheel load location method based on vehicle license plate detection and multiple projective planes, and the accurate positioning of the vehicle center is achieved by the projective relationship matrix of different planes. Then, accurate measurement of the lateral wheelbase is achieved through secondary detection and projective transformation. Further, accurate wheel load tracking for fatigue research is achieved by the multi-objective tracking algorithm. Based on theoretical analysis and practical application results, the effectiveness and accuracy of this method have been verified. Different from traditional positioning methods based on vehicle detection boxes and 3D reconstruction boxes, the proposed method has higher accuracy and will play a fundamental role in the use of vehicle load spatiotemporal data for more accurate analysis such as fatigue research.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"31 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050875","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}
X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang
This paper addresses the challenge of real-time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is established, resulting in optimal trajectories, the sensitive parameters, and the corresponding variation ranges. Second, an RL model is built, and the optimization results obtained offline are used as training samples. The RL-based method can be applied for continuous digging tasks, which is beneficial for improving the overall efficiency of the autonomous operation of the excavator. Finally, simulation experiments were conducted in four distinct conditions. The results demonstrate that the proposed method effectively accomplishes excavation tasks, with trajectory generation completed within 0.5 s. Comprehensive performance metrics remained below 0.14, and the excavation rate exceeded 92%, surpassing or matching the performance of the optimization-based method and PINN-based method. Moreover, the proposed method produced consistently balanced trajectory performance across all sub-tasks. These results underline the method's effectiveness in achieving real-time, multi-objective, and continuous trajectory planning for autonomous excavators.
{"title":"Reinforcement learning-based trajectory planning for continuous digging of excavator working devices in trenching tasks","authors":"X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang","doi":"10.1111/mice.13428","DOIUrl":"https://doi.org/10.1111/mice.13428","url":null,"abstract":"This paper addresses the challenge of real-time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is established, resulting in optimal trajectories, the sensitive parameters, and the corresponding variation ranges. Second, an RL model is built, and the optimization results obtained offline are used as training samples. The RL-based method can be applied for continuous digging tasks, which is beneficial for improving the overall efficiency of the autonomous operation of the excavator. Finally, simulation experiments were conducted in four distinct conditions. The results demonstrate that the proposed method effectively accomplishes excavation tasks, with trajectory generation completed within 0.5 s. Comprehensive performance metrics remained below 0.14, and the excavation rate exceeded 92%, surpassing or matching the performance of the optimization-based method and PINN-based method. Moreover, the proposed method produced consistently balanced trajectory performance across all sub-tasks. These results underline the method's effectiveness in achieving real-time, multi-objective, and continuous trajectory planning for autonomous excavators.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050874","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}
Gaining insights into pedestrian flow patterns in public spaces can greatly benefit decision-making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force-based technique characterized by a loosely defined leader–follower structure. First, a complex field-based phase transfer entropy (PTE) method was applied to measure the difference in information flow between pedestrians. Setting the detecting threshold with the 3 sigma principle, the radial basis function (RBF) was utilized to identify the leader in the collective. Integrating the PTE, RBF, and social force model (SFM), a comprehensive model (PTE-RBF-SFM) was developed to simulate collective behavior. Some bidirectional pedestrian flow data, collected from Fairground Düsseldorf, were used to validate the model in a real-world setting. The results showed that the proposed model provided more realistic trajectories than benchmark models, and the spontaneous leader–follower structure was found to change over time and stable with time interval prolonging.
{"title":"Modeling the collective behavior of pedestrians with the spontaneous loose leader–follower structure in public spaces","authors":"Jie Xu, Dengyu Xu, Jing Wu, Xiaowei Shi","doi":"10.1111/mice.13429","DOIUrl":"https://doi.org/10.1111/mice.13429","url":null,"abstract":"Gaining insights into pedestrian flow patterns in public spaces can greatly benefit decision-making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force-based technique characterized by a loosely defined leader–follower structure. First, a complex field-based phase transfer entropy (PTE) method was applied to measure the difference in information flow between pedestrians. Setting the detecting threshold with the 3 sigma principle, the radial basis function (RBF) was utilized to identify the leader in the collective. Integrating the PTE, RBF, and social force model (SFM), a comprehensive model (PTE-RBF-SFM) was developed to simulate collective behavior. Some bidirectional pedestrian flow data, collected from Fairground Düsseldorf, were used to validate the model in a real-world setting. The results showed that the proposed model provided more realistic trajectories than benchmark models, and the spontaneous leader–follower structure was found to change over time and stable with time interval prolonging.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"34 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044282","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}
Precise three-dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these limitations, this paper introduces StructNet3D, a point cloud neural network specifically designed for instance segmentation in indoor components including ceilings, floors, and walls. StructNet3D employs a novel multi-scale 3D U-Net backbone integrated with ArchExtract, which designed to capture both global context and local structural details, enabling precise segmentation of diverse indoor environments. Compared to other methods, StructNet3D achieved an AP50 of 87.7 on the proprietary dataset and 68.6 on the S3DIS dataset, demonstrating its effectiveness in accurately segmenting and classifying major structural components within diverse indoor environments.
{"title":"Enhanced three-dimensional instance segmentation using multi-feature extracting point cloud neural network","authors":"Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong Wu","doi":"10.1111/mice.13430","DOIUrl":"https://doi.org/10.1111/mice.13430","url":null,"abstract":"Precise three-dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these limitations, this paper introduces StructNet3D, a point cloud neural network specifically designed for instance segmentation in indoor components including ceilings, floors, and walls. StructNet3D employs a novel multi-scale 3D U-Net backbone integrated with ArchExtract, which designed to capture both global context and local structural details, enabling precise segmentation of diverse indoor environments. Compared to other methods, StructNet3D achieved an AP50 of 87.7 on the proprietary dataset and 68.6 on the S3DIS dataset, demonstrating its effectiveness in accurately segmenting and classifying major structural components within diverse indoor environments.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"58 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026362","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 Modeling of spatially embedded networks via regional spatial graph convolutional networks by Jürgen Hackl et al., https://doi.org/10.1111/mice.13286.
{"title":"Cover Image, Volume 40, Issue 4","authors":"","doi":"10.1111/mice.13426","DOIUrl":"10.1111/mice.13426","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Modeling of spatially embedded networks via regional spatial graph convolutional networks</i> by Jürgen Hackl et al., https://doi.org/10.1111/mice.13286.\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 4","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020912","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}
Particle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle geometry. Noise algorithms are utilized to apply random variations with certain morphological patterns on the surface of the base geometry (e.g., a sphere), thereby generating a variety of particles with morphological patterns ranging from very angular to rounded. In addition, the base geometry can be replaced with other geometries including real particle scans, allowing rapid generation of realistic particles with morphological characteristics of the base geometry. The framework stands out for its simplicity, the wide range of particle morphologies generated, reducing the need for extensive computation and scanning, and provides a new idea for the granular soil behavior simulations.
{"title":"A noise‐based framework for randomly generating soil particle with realistic geometry","authors":"Chen‐Xi Tong, Jia‐Jun Li, Quan Sun, Sheng Zhang, Wan‐Huan Zhou, Daichao Sheng","doi":"10.1111/mice.13424","DOIUrl":"https://doi.org/10.1111/mice.13424","url":null,"abstract":"Particle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle geometry. Noise algorithms are utilized to apply random variations with certain morphological patterns on the surface of the base geometry (e.g., a sphere), thereby generating a variety of particles with morphological patterns ranging from very angular to rounded. In addition, the base geometry can be replaced with other geometries including real particle scans, allowing rapid generation of realistic particles with morphological characteristics of the base geometry. The framework stands out for its simplicity, the wide range of particle morphologies generated, reducing the need for extensive computation and scanning, and provides a new idea for the granular soil behavior simulations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"44 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989103","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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