Despite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in bridge point cloud segmentation. In this paper, a universal multi-type bridge point cloud databank, named BrPCD, consisting of a total of 98 point cloud data (PCD; 10 of them are obtained from scanning, and the rest is obtained by data augmentation) from small to long-span bridges, is established. Additionally, a method for augmenting bridge PCD is proposed, significantly enriching the spatial feature information of bridges within the dataset. Furthermore, based on the introduced data annotation rules, a uniform categorization of semantic labels for bridge components is implemented, enhancing the applicability of our dataset across various semantic segmentation tasks for different types of bridges. A benchmark testing was conducted on the BrPCD using the PointNet model. The segmentation results indicate that the parameters learned through the BrPCD enable accurate segmentation at the level of various types of bridge components. In other words, the BrPCD can function as a universal dataset, applicable for testing various networks aimed at bridge point cloud segmentation.
{"title":"A bridge point cloud databank for digital bridge understanding","authors":"Hongwei Zhang, Yanjie Zhu, Wen Xiong, C. S. Cai","doi":"10.1111/mice.13384","DOIUrl":"https://doi.org/10.1111/mice.13384","url":null,"abstract":"Despite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in bridge point cloud segmentation. In this paper, a universal multi-type bridge point cloud databank, named BrPCD, consisting of a total of 98 point cloud data (PCD; 10 of them are obtained from scanning, and the rest is obtained by data augmentation) from small to long-span bridges, is established. Additionally, a method for augmenting bridge PCD is proposed, significantly enriching the spatial feature information of bridges within the dataset. Furthermore, based on the introduced data annotation rules, a uniform categorization of semantic labels for bridge components is implemented, enhancing the applicability of our dataset across various semantic segmentation tasks for different types of bridges. A benchmark testing was conducted on the BrPCD using the PointNet model. The segmentation results indicate that the parameters learned through the BrPCD enable accurate segmentation at the level of various types of bridge components. In other words, the BrPCD can function as a universal dataset, applicable for testing various networks aimed at bridge point cloud segmentation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"19 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696505","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}
Structural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated seismic event detection procedure with interpretability and robustness. The sensor-wise raw time series is transformed into image data, enhancing the separability of classification while endowing with visual understandability. Vision Transformers (ViTs) and Residual Networks (ResNets) aided by a heat map–based visual interpretation technique are used for image classification. Multitype faulty data that could disturb the seismic event detection are considered in the classification. Then, divergent results from multiple sensors are fused by Bayesian fusion, outputting a consistent seismic detection result. A real-world monitoring data set of four seismic responses of a pair of long-span bridges is used for method validation. At the classification stage, ResNet 34 achieved the best accuracy of over 90% with minimal training cost. After Bayesian fusion, globally consistent and accurate seismic detection results can be obtained using a ResNet or ViT. The proposed approach effectively localizes seismic events within multisource, multifault monitoring data, achieving automated and consistent seismic event detection.
结构健康监测（SHM）旨在评估民用基础设施的性能并确保安全。从大量连续监测数据中自动检测地震等现场事件，对于确保后续数据分析的及时性非常重要。为了克服人工识别的不及时性和传感器的不一致性，本文提出了一种具有可解释性和鲁棒性的地震事件自动检测程序。将传感器的原始时间序列转换为图像数据，增强了分类的可分离性，同时赋予了视觉上的可理解性。视觉转换器（ViTs）和残差网络（ResNets）在基于热图的视觉解读技术的辅助下用于图像分类。在分类过程中，考虑了可能干扰地震事件检测的多种错误数据。然后，通过贝叶斯融合法融合来自多个传感器的不同结果，输出一致的地震检测结果。实际监测数据集包括一对大跨度桥梁的四次地震响应，用于方法验证。在分类阶段，ResNet 34 以最小的训练成本达到了 90% 以上的最佳准确率。贝叶斯融合后，使用 ResNet 或 ViT 可以获得全局一致且准确的地震检测结果。所提出的方法能有效定位多源、多断层监测数据中的地震事件，实现自动、一致的地震事件检测。
{"title":"Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion","authors":"Zhiyi Tang, Jiaxing Guo, Yinhao Wang, Wei Xu, Yuequan Bao, Jingran He, Youqi Zhang","doi":"10.1111/mice.13377","DOIUrl":"https://doi.org/10.1111/mice.13377","url":null,"abstract":"Structural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated seismic event detection procedure with interpretability and robustness. The sensor-wise raw time series is transformed into image data, enhancing the separability of classification while endowing with visual understandability. Vision Transformers (ViTs) and Residual Networks (ResNets) aided by a heat map–based visual interpretation technique are used for image classification. Multitype faulty data that could disturb the seismic event detection are considered in the classification. Then, divergent results from multiple sensors are fused by Bayesian fusion, outputting a consistent seismic detection result. A real-world monitoring data set of four seismic responses of a pair of long-span bridges is used for method validation. At the classification stage, ResNet 34 achieved the best accuracy of over 90% with minimal training cost. After Bayesian fusion, globally consistent and accurate seismic detection results can be obtained using a ResNet or ViT. The proposed approach effectively localizes seismic events within multisource, multifault monitoring data, achieving automated and consistent seismic event detection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"73 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678353","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}
Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao
Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full-stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub-pixel level, with pixel-based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub-pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub-pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS-Silica features easy data acquisition, high precision, and long service term, offering a promising method for long-term measurement of both surface and internal structures.
{"title":"Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position","authors":"Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao","doi":"10.1111/mice.13383","DOIUrl":"10.1111/mice.13383","url":null,"abstract":"<p>Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full-stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub-pixel level, with pixel-based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub-pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub-pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS-Silica features easy data acquisition, high precision, and long service term, offering a promising method for long-term measurement of both surface and internal structures.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 24","pages":"3805-3820"},"PeriodicalIF":8.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673114","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}
S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi
Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.
{"title":"Reinforcement learning-based approach for urban road project scheduling considering alternative closure types","authors":"S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi","doi":"10.1111/mice.13365","DOIUrl":"https://doi.org/10.1111/mice.13365","url":null,"abstract":"Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"46 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673123","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 A multi-phase mechanical model of biochar–cement composites at the mesoscale by Muduo Li et al., https://doi.org/10.1111/mice.13307.
封面图片来自 Muduo Li 等人撰写的《中尺度生物炭-水泥复合材料的多相力学模型》一文，https://doi.org/10.1111/mice.13307。
{"title":"Cover Image, Volume 39, Issue 23","authors":"","doi":"10.1111/mice.13380","DOIUrl":"10.1111/mice.13380","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>A multi-phase mechanical model of biochar–cement composites at the mesoscale</i> by Muduo Li et al., https://doi.org/10.1111/mice.13307.\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":"39 23","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665227","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}
Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao
This study proposes a multi-perspective fusion model for operating speed prediction based on knowledge-enhanced graph neural networks, named RoadGNN-S. By utilizing message passing and multi-head self-attention mechanisms, RoadGNN-S can effectively capture the coupling impacts of multi-perspective alignment elements (i.e., two-dimensional design, 2.5-dimensional driving, and three-dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and R-squared values of RoadGNN-S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN-S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data-driven models, knowledge-enhanced RoadGNN-S effectively avoids the fundamental errors, improving the R-squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi-perspective fusion and knowledge enhancement techniques.
本研究提出了一种基于知识增强图神经网络的运行速度预测多视角融合模型，命名为 RoadGNN-S。通过利用消息传递和多头自关注机制，RoadGNN-S 可以有效捕捉多视角排列元素（即二维设计、2.5 维驾驶和三维空间视角）的耦合影响。驾驶模拟数据结果表明，RoadGNN-S 的均方根误差、平均绝对误差、平均绝对百分比误差和 R 平方值均优于其他经典深度学习算法。然后，在 RoadGNN-S 中引入先验知识（即公路几何供给、驾驶员期望和车辆动态），并通过现场观测实验验证了模型的预测准确性和可移植性。与上述数据驱动模型相比，知识增强型 RoadGNN-S 有效避免了基本误差，在预测乘用车和卡车运行速度方面的 R 平方值分别提高了 7.9% 和 10.7%。该研究结果有助于利用多视角融合和知识增强技术进行智能公路几何设计。
{"title":"A multi-perspective fusion model for operating speed prediction on highways using knowledge-enhanced graph neural networks","authors":"Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao","doi":"10.1111/mice.13382","DOIUrl":"https://doi.org/10.1111/mice.13382","url":null,"abstract":"This study proposes a multi-perspective fusion model for operating speed prediction based on knowledge-enhanced graph neural networks, named RoadGNN-S. By utilizing message passing and multi-head self-attention mechanisms, RoadGNN-S can effectively capture the coupling impacts of multi-perspective alignment elements (i.e., two-dimensional design, 2.5-dimensional driving, and three-dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and <i>R</i>-squared values of RoadGNN-S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN-S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data-driven models, knowledge-enhanced RoadGNN-S effectively avoids the fundamental errors, improving the R-squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi-perspective fusion and knowledge enhancement techniques.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"76 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665228","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}
Real-time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost-effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short-term memory (LSTM) network, termed CATS-LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS-LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS-LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS-LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.
{"title":"Adaptive compensation using long short-term memory networks for improved control performance in real-time hybrid simulation","authors":"Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun Zhang","doi":"10.1111/mice.13378","DOIUrl":"https://doi.org/10.1111/mice.13378","url":null,"abstract":"Real-time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost-effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short-term memory (LSTM) network, termed CATS-LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS-LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS-LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS-LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642664","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}
Donatella Gubiani, Giovanni Sgrazzutti, Massimiliano Basso, Elena Viero, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto
Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 <span data-altimg="/cms/asset/050068ec-5413-4a29-bb86-5a80bb52ff3f/mice13376-math-0001.png"></span><mjx-container ctxtmenu_counter="17" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/mice13376-math-0001.png"><mjx-semantics><mjx-msup data-semantic-children="0,1" data-semantic- data-semantic-role="unknown" data-semantic-speech="k m squared" data-semantic-type="superscript"><mjx-mi data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="unknown" data-semantic-type="identifier"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mi><mjx-script style="vertical-align: 0.421em;"><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number" size="s"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:10939687:media:mice13376:mice13376-math-0001" display="inline" location="graphic/mice13376-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup data-semantic-="" data-semantic-children="0,1" data-semantic-role="unknown" data-semantic-speech="k m squared" data-semantic-type="superscript"><mi data-semantic-="" data-semantic-font="normal" data-semantic-parent="2" data-semantic-role="unknown" data-semantic-type="identifier">km</mi><mn data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number">2</mn></msup>${rm km}^2$</annotation></semantics></math></mjx-assistive-mml></mjx-container>. This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical feat
{"title":"A dynamic neural network model for the identification of asbestos roofings in hyperspectral images covering a large regional area","authors":"Donatella Gubiani, Giovanni Sgrazzutti, Massimiliano Basso, Elena Viero, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto","doi":"10.1111/mice.13376","DOIUrl":"https://doi.org/10.1111/mice.13376","url":null,"abstract":"Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 <span data-altimg=\"/cms/asset/050068ec-5413-4a29-bb86-5a80bb52ff3f/mice13376-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"17\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13376-math-0001.png\"><mjx-semantics><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"k m squared\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.421em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13376:mice13376-math-0001\" display=\"inline\" location=\"graphic/mice13376-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"unknown\" data-semantic-speech=\"k m squared\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\">km</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\">2</mn></msup>${rm km}^2$</annotation></semantics></math></mjx-assistive-mml></mjx-container>. This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical feat","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"35 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610563","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 study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.
{"title":"Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment","authors":"Chao Liu, Hongfei Jia, Guanfeng Wang, Ruiyi Wu, Jingjing Tian, Heyao Gao","doi":"10.1111/mice.13371","DOIUrl":"https://doi.org/10.1111/mice.13371","url":null,"abstract":"This study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"33 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599250","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}
Jun S. Lee, Jeongjun Park, Hyun Min Kim, Robin Eunju Kim
A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.
{"title":"Damage detection for railway bridges using time‐frequency decomposition and conditional generative model","authors":"Jun S. Lee, Jeongjun Park, Hyun Min Kim, Robin Eunju Kim","doi":"10.1111/mice.13372","DOIUrl":"https://doi.org/10.1111/mice.13372","url":null,"abstract":"A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599251","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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