New Developments in Intelligent Construction and Operation of Infrastructures

Topic Information

Dear Colleagues,

Infrastructure plays an important role in human activities. Improving the safety and durability of infrastructure becomes a major challenge in the economic development process. The combination of artificial intelligence with engineering construction, structural health monitoring, condition assessment, and performance improvement is an essential way to realize the intelligent construction and operation of infrastructure, which has become a research hotspot and difficulty involving multiple disciplines. At present, with the rapid development of the Internet of Things, big data, artificial intelligence, and remote sensing, it is inevitable to explore new technologies covering the intelligent construction and operation of infrastructures in the life cycle. For this reason, this topic aims to boost knowledge and development in the intelligent construction and operation of infrastructures through multi-disciplinary works.

The potential topics include (but are not limited to):

• Intelligent structural design;
• Intelligent construction;
• Intelligent operation and maintenance;
• Intelligent disaster prevention;
• Structural earthquake and wind engineering;
• New technologies in the life cycle of infrastructures.

Prof. Dr. Jingzhou Xin
Dr. Yan Jiang
Dr. Fengbo Wu
Dr. Runchuan Xia
Prof. Dr. Simon X. Yang
Dr. Qizhi Tang
Dr. Yonghui Fan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
CivilEng civileng	-	2.8	2020	24.4 Days	CHF 1200	Submit
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600	Submit

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Published Papers (6 papers)

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All Algorithms Buildings Sensors CivilEng JMSE

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29 pages, 8423 KiB  

Open AccessArticle

Research on the Support-Free Replacement Method of Suspenders for Long-Span Self-Anchored Rail Special Suspension Bridges

by Xiaogang Li, Minglin Zhou, Peng Ding, Ling Luo, Xiangsheng Huang and Xiang Li

Buildings 2025, 15(9), 1406; https://doi.org/10.3390/buildings15091406 - 22 Apr 2025

Viewed by 82

Abstract

To meet the demand of not interrupting traffic during the replacement of suspenders in long-span railway suspension bridges, this research proposes for the first time the application of the unsupported replacement method to the suspender replacement of self-anchored railway suspension bridges. Based on [...] Read more.

To meet the demand of not interrupting traffic during the replacement of suspenders in long-span railway suspension bridges, this research proposes for the first time the application of the unsupported replacement method to the suspender replacement of self-anchored railway suspension bridges. Based on the basic principle of suspension bridge, the safety control index in the process of boom replacement is proposed. Midas Civil 2024 software is used to analyze the structural response of the boom after removal under static force and train load, including the change of cable force of adjacent boom, the displacement of main cable and stiffening beam. The real bridge test was carried out based on the special bridge of Chongqing Egongyan Track. The results show that after the removal of the boom, the cable force of the adjacent boom increases by 42–55%, the main cable is partially twisted but the adjacent joints change little, and the displacement of the stiffened beam meets the specification requirements. When the train is fully loaded, the maximum increase of the cable force of the adjacent boom is 150 kN, the stress increment of the operating boom is far less than the design strength, the increase of the downtorsion of the main cable is only 2.22%, and the displacement of the stiffening beam is within the allowable range. The safety control index and real bridge test results show that the unsupported replacement method is feasible and safe in the replacement of the suspenders of long-span rail suspension bridges, which provides an important reference for related projects. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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18 pages, 3311 KiB  

Open AccessArticle

Synchronous Multi-Span Closure Techniques in Continuous Rigid-Frame Bridges: Research and Implementation

by Xinyu Yao and Chuanxi Li

Buildings 2025, 15(8), 1331; https://doi.org/10.3390/buildings15081331 - 17 Apr 2025

Viewed by 124

Abstract

This study investigates the Huangdong Daning River Bridge project in Guangxi, where the innovative side-span and mid-span synchronous closure technology for continuous rigid-frame bridges (CRFB) was systematically implemented for the first time in this region of China. A comparative finite element model developed [...] Read more.

This study investigates the Huangdong Daning River Bridge project in Guangxi, where the innovative side-span and mid-span synchronous closure technology for continuous rigid-frame bridges (CRFB) was systematically implemented for the first time in this region of China. A comparative finite element model developed in MIDAS Civil 2024 was employed to analyze the mechanical behavior mechanisms of main girders under span-by-span closure and synchronous closure processes. The numerical simulation results demonstrate that the stress distribution in main girders shows no significant sensitivity (<3%) to closure method differences during both the bridge completion phase and 10-year shrinkage-creep cycle. However, distinct closure sequences (asynchronous vs. synchronous) exhibited notable impacts on the girder alignment at the completion stage. The cumulative deviation induced by differential installation elevations of formwork segments necessitates precise dynamic control during construction monitoring. Furthermore, shrinkage and creep effects manifested differential influences on pier top horizontal displacements and bending moments when employing different closure methods, though all variations remained within 5%. The synchronous multi-span closure technology effectively mitigates structural mutation risks during construction while achieving superior alignment accuracy, rational stress distribution, and accelerated construction progress as verified by field implementation. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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21 pages, 8531 KiB  

Open AccessArticle

Recursive Time Series Prediction Modeling of Long-Term Trends in Surface Settlement During Railway Tunnel Construction

by Feilian Zhang, Qicheng Wei, Zhe Wu, Jiawei Cao, Danlin Jian and Lantian Xiang

CivilEng 2025, 6(2), 19; https://doi.org/10.3390/civileng6020019 - 3 Apr 2025

Viewed by 232

Abstract

The surface settlement of railroad tunnels is dynamically updated as the construction progresses, exhibiting complex nonlinear characteristics. The accuracy of the on-site nonlinear regression fitting prediction method needs to be improved. To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, [...] Read more.

The surface settlement of railroad tunnels is dynamically updated as the construction progresses, exhibiting complex nonlinear characteristics. The accuracy of the on-site nonlinear regression fitting prediction method needs to be improved. To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, while also ensuring the safety of the tunnel and existing structures, we propose a recursive prediction model for the long-term trend of surface settlement utilizing a singular spectrum analysis (SSA), improved sand cat swarm optimization (ISCSO), and a kernel extreme learning machine (KELM). First, SSA decomposition, known for its adaptive decomposition of one-dimensional nonlinear time series, reorganizes the early surface settlement data. The dynamic sliding window method is introduced to construct the prediction dataset, which is then trained using the KELM. ISCSO is used to optimize the key parameters of the KELM to obtain the long-term trend curves of surface settlement through recursive time series prediction. The superiority and effectiveness of ISCSO and the model are verified through numerical experiments and simulation experiments based on engineering cases, providing a reference for the early warning and control of surface settlement during the construction of similar tunnels. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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25 pages, 11695 KiB  

Open AccessArticle

Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network

by Liming Zhou, Haowen Jia, Shang Jiang, Fei Xu, Hao Tang, Chao Xiang, Guoqing Wang, Hemin Zheng and Lingkun Chen

Buildings 2025, 15(7), 1117; https://doi.org/10.3390/buildings15071117 - 29 Mar 2025

Viewed by 380

Abstract

Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection [...] Read more.

Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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24 pages, 2227 KiB  

Open AccessReview

Current Status and Prospects of Digital Twin Approaches in Structural Health Monitoring

by Qiuting Wang, Bo Huang, Yongsheng Gao and Chaojian Jiao

Buildings 2025, 15(7), 1021; https://doi.org/10.3390/buildings15071021 - 22 Mar 2025

Viewed by 497

Abstract

Structural health monitoring (SHM) is a critical technology for ensuring infrastructure safety, extending their service life, and reducing their maintenance costs. With the rapid development of digital twin (DT) technology, an increasing number of studies have implemented DT in SHM systems. This study [...] Read more.

Structural health monitoring (SHM) is a critical technology for ensuring infrastructure safety, extending their service life, and reducing their maintenance costs. With the rapid development of digital twin (DT) technology, an increasing number of studies have implemented DT in SHM systems. This study provides a detailed analysis of the role of DT in SHM through a comprehensive literature review, specifically examining its applications in damage detection, dynamic response monitoring, and maintenance management. The paper first reviews advances in DT applications across various fields, then systematically discusses how DT enhances monitoring accuracy, enables real-time performance, and supports predictive maintenance strategies in SHM. Finally, technical challenges and future research directions for DT implementation in SHM are explored. The findings highlight DT’s significant potential to improve both the efficiency and the accuracy of structural monitoring systems, while proposing innovative solutions for intelligent infrastructure management. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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18 pages, 3218 KiB  

Open AccessArticle

CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer

by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu and Yuantong Xu

Buildings 2025, 15(2), 197; https://doi.org/10.3390/buildings15020197 - 10 Jan 2025

Cited by 1 | Viewed by 795

Abstract

Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature [...] Read more.

Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks. Full article

(This article belongs to the Topic New Developments in Intelligent Construction and Operation of Infrastructures)

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