Journal Menu

Special Issues

Table of Content

AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience

Submission Deadline: 30 June 2026 (closed) View: 1706 Submit to Special Issue

Guest Editor(s)

Assoc. Prof. Xiao Tan

Email: xiaotan@hhu.edu.cn

Affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 211100, China

Homepage:

Research Interests: structural health monitoring, fiber optic sensors, urban lifelines, intelligent infrastructure, multifunctional construction materials

图片1.png

Prof. Lu Zhang

Email: zhanglu@glut.edu.cn

Affiliation: School of Civil Engineering, Guilin University of Technology, Guilin 541006, China

Homepage:

Research Interests: structural health monitoring and nondestructive evaluation, smart materials and structures, wave propagation, guided ultrasonic waves, and acoustoelasticity, structural resilience and earthquake engineering, acoustic emission

图片2.png

Assoc. Prof. Dan Li

Email: lidan@seu.edu.cn

Affiliation: School of Civil Engineering, Southeast University, Nanjing 210096, China

Homepage:

Research Interests: structural health monitoring and nondestructive evaluation, acoustic emission and ultrasonic testing, smart construction and mantainace, machine learning and data processing

图片3.png

Assoc. Prof. E Chen

Email: chene@hust.edu.cn

Affiliation: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Homepage:

Research Interests: monitoring and assessment of steel corrosion in concrete

图片4.png

Assist. Prof. Li Ai

Email: ail@email.sc.edu

Affiliation: Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA

Homepage:

Research Interests: nondestructive testing, structural health monitoring, bridge load rating, AI for science/engineering, cyber-physical systems, digital twins

图片5.png

Summary

In modern urban environments, the health of structural infrastructure – encompassing roads, bridges, tunnels, and other critical elements – is crucial for the everyday functioning and well-being of city residents. These infrastructures, key to seamless operation and economic vitality, are currently facing challenges from climate change, urbanization, aging, and natural hazards. The development of structural health monitoring (SHM), non-destructive testing (NDT), digital twins, and novel functional construction materials has become critical. Artificial Intelligence (AI) is increasingly central in these areas, offering advanced solutions for real-time monitoring, condition assessment, and ensuring the resilience of these vital infrastructures. The encouraged topics include but are not limited to:
1. Recent advancements in structural health monitoring and sensor technologies.
2. State-of-the-art non-destructive testing methods
3. Digital twins and AI methods relevant to resilient infrastructures.
4. Intelligent constructional materials with self-sensing and adaptive functionalities.
5. Modern data processing techniques for sustainable and recycling material practices.

Keywords

artificial intelligence; advanced sensing and monitoring technologies; non-destructive testing and evaluation; data analysis; intelligent constructional materials

Published Papers

Show export options

Open Access

ARTICLE

Baseline-Free Damage Localization in Periodic Cable-Net Structures Using a Two-Dimensional Recursive Finite Element Method
Lu Zhang, Guifu Jia, Haowei Xu, Jian Zeng
Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2026.082602
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract Large-span cable-net roof systems used in public infrastructure require efficient and reliable inspection or structural health monitoring because local prestress loss is frequently observed, which significantly affects structural safety and mechanical performance. To address the high computational cost of dynamic analysis and over-reliance on baseline in conventional methods, this study proposes a baseline-free damage localization framework for two-dimensional periodic cable-net structures by integrating periodic structural design, recursive finite element analysis, and frequency-response-based identification. First, a square periodic cable-net unit cell with crossed diagonal cables is established, and the two-dimensional dispersion curve considering initial pre-stress is… More >

View
344

Download
76
Open Access

ARTICLE

Combining Principal Component Analysis and Multilayer Perceptron to Establish a Construction Quality Prediction Model
Ching-Lung Fan
Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081046
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract Quality attainment in public construction projects is paramount for effective project management. This study employs Principal Component Analysis (PCA) in the initial phase to discern noncollinear critical defects from inspections across 1015 projects. The identified components are categorized into three aspects: Inspection Records and Occupational Safety and Health (Aspect I), Concrete Quality (Aspect II), and Construction Team Quality Management (Aspect III). Subsequently, a Multilayer Perceptron (MLP) network, trained on 13 PCA-identified critical defects, transforms input data into a probability, indicating project quality. The MLP model exhibits exceptional performance with 91.3% accuracy, 90.2% precision, and 96.1% More >

View
281

Download
71
Open Access

ARTICLE

Manufacturing of CFRP Plate Coupled with Large Range FBG
Mingxia Li, Haowei Xu, Ruiqi Li, Bo Song, Wanxu Zhu
Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.075587
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract Conventional fiber Bragg grating (FBG) sensors used for tensile monitoring have a limited measurement range and therefore cannot cover the entire service stage of prestressed carbon-fiber-reinforced polymer (CFRP)–strengthened members. In this study, a smart CFRP plate is developed by embedding a wide-range FBG sensor in a prestressed CFRP plate. Based on strain-transfer theory for the grating region, an analytical expression for the average strain-transfer rate is derived and then used to inversely design the groove geometry and bonding parameters; the resulting groove size is 0.5 mm × 0.5 mm. During bonding, a tensile force of… More >

View
1327

Download
638
Open Access

ARTICLE

Diffusion-Driven Generation of Synthetic Complex Concrete Crack Images for Segmentation Tasks
Pengwei Guo, Xiao Tan, Yiming Liu
Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071317
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train… More >

View
2263

Download
1016
Open Access

ARTICLE

Segmentation of Building Surface Cracks by Incorporating Attention Mechanism and Dilation-Wise Residual
Yating Xu, Mansheng Xiao, Mengxing Gao, Zhenzhen Liu, Zeyu Xiao
Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822
（This article belongs to the Special Issue: AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience)
Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >

View
1368

Download
708

AI-driven Monitoring, Condition Assessment, and Data Analytics for Enhancing Infrastructure Resilience

Guest Editor(s)

Summary

Keywords

Published Papers

View

344

Download

76

View

281

Download

71

View

1327

Download

638

View

2263

Download

1016

View

1368

Download

708

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link