Yongning Zhang¹, Dongxue Li^1,2,*, Cen Yang³, Yongwang Gui⁴

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081767 - 24 August 2026

Abstract To address the challenge that the baseline state of FE models for operational highly statically indeterminate bridges is difficult to evaluate accurately, this paper proposes an intelligent multi-parameter inversion and updating framework driven by measured dynamic strains and a LSTM neural network. First, to tackle the complex environmental interferences coupled within short-term monitoring strain signals, a moving-window baseline detrending and refined thermal effect decoupling algorithm is employed. This successfully strips away long-term dead loads and temperature drift, extracting pure mechanical strain sequences with a high signal-to-noise ratio. Second, to overcome the mode omission issue caused… More >

Yongjun Lu¹, Zhili Guo², Yongze Ye³, Xin Liu⁴, Yao Jin⁵, Xiang Xu^4,6,*

Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.078396 - 30 June 2026

Abstract To fully leverage structural health monitoring data for bridge condition assessment, this study proposes a comprehensive evaluation method that integrates static and dynamic indicators using monitoring data, and demonstrates its feasibility through a short-term monitoring-based trend analysis on a newly built bridge. First, based on statistical principles, the Weibull distribution is employed to extract the dead-load component from static monitoring data. Building upon this, a static performance evaluation method is established by incorporating spatial uniformity and trend non-uniformity coefficients. Subsequently, spectral analysis is performed on the main girder acceleration data to extract fundamental frequency information… More >

Liqiong Chen¹, Haoyu Jia¹, Mailun Liu², Kai Zhang^1,*, Song Yang¹, Zongjun Jiang¹

Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.077675 - 30 June 2026

Abstract Buried natural gas pipelines are critical components of energy infrastructure, and their durability and safe operation depend on effective structural health monitoring and the early identification of damage states. In farmland environments, rotary tillage imposes repeated and often concealed mechanical loads on buried pipelines, resulting in stress accumulation, progressive deterioration, and potentially structural failure. However, predictive and interpretable health monitoring approaches that explicitly incorporate rotary tiller-induced damage mechanisms remain scarce. In this study, a physics-informed and interpretable hybrid framework is proposed for the structural health monitoring of buried pipelines subjected to rotary tiller loading. A… More >

Guansi Liu¹, Hui Jin^1,*, Keqin Ding², Hao Wang³, Violeta Mircevska⁴, Maosen Cao⁵

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.079074 - 18 May 2026

Abstract In response to the dynamic changes in fatigue damage location of crane welding structures under lifting loads and the difficulty in accurately obtaining the stress concentration factor of welds, which results in limited effetiveness of traditional health monitoring sensor placement. This paper proposes aa sensor placement optimization method that integrates damage prediction and risk assessment. Firstly, the influence of weld geometry on fatigue performance is analyzed, and a rapid estimation model for the stress concentration factor is established using a radial basis function support vector machine. Furthermore, a fatigue damage prediction model for the welded… More >

Abdullah Alariyan¹, Abdulhadi Alzabout², Mohammed Alariyan³, Anas Alaryan⁴, Mahmoud Alhashash⁵, Abdulrahman Ahmed⁶, Mohammed Abdulaal⁷, Ahed Habib^8,*

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.076012 - 18 May 2026

Abstract Unsupervised machine learning has recently gained attention in structural health monitoring as engineers seek methods that can interpret large and complex data sets without prior labeling. Traditional diagnostic approaches often rely on predefined models or manual analysis, which limits their adaptability and efficiency when dealing with evolving structural behaviors or unforeseen conditions. Despite the growing interest in this domain, the literature remains fragmented, with limited systematic and bibliometric reviews that consolidate progress, identify prevailing trends, and clarify methodological limitations. This study addresses this gap through a comprehensive systematic and bibliometric review of research on unsupervised More >

Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Giada Faraco^*, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro^*, Arcangelo Messina

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.075327 - 31 March 2026

Abstract Damage detection and localization analysis have gained increasing importance over the years, due to the growing number of catastrophic events and the associated risks that small, undetected cracks in structures may evolve into severe failures if not identified in time. In this context, vibration-based methods have been extensively investigated for structural damage detection. Among them, one of the most widely used approaches since its introduction is the curvature method. It has been successfully employed in numerous studies, consistently providing reliable results. However, the use of second-order or higher-order derivatives can be challenging when dealing with… More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073949 - 31 March 2026

Abstract The study of long-term pavement performance is a fundamental topic in the field of highway engineering. Through comprehensive and in-depth research on the pavement system, the previous scattered, one-sided, superficial, and perceptual knowledge and experience are summarized and sublimated into a systematic and complete engineering theory, thereby providing powerful guidance and assistance for the practice of pavement design, construction, maintenance, operation, and management. In this research, the mentoring system deployment technology for automatic monitoring is carried out for long-term pavement performance. By burying a variety of sensors in different parts of the road surface, base,… More >

Mohammadreza Vafaei^1,*, Sophia C. Alih², Abdirahman Abdulkadir¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078250 - 30 March 2026

Abstract Rapid quantification of seismic-induced damage immediately following an earthquake is critical for determining whether a structure is safe for continued occupation or requires evacuation. This study proposes a novel damage identification method that utilizes limited strain data points, significantly reducing installation, maintenance, and data analysis costs compared to traditional distributed sensor networks. The approach integrates finite element (FE) modeling to generate capacity curves through pushover analysis, incorporates noise-augmented datasets for Artificial Neural Network (ANN) training, and classifies structural conditions into four damage levels: Operational (OP), Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP).… More > Graphic Abstract

Tao Jin^1,2, Zhekun Shou¹, Hongchao Liu^1,*, Yuchun Shao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076415 - 26 February 2026

Abstract This research centers on structural health monitoring of bridges, a critical transportation infrastructure. Owing to the cumulative action of heavy vehicle loads, environmental variations, and material aging, bridge components are prone to cracks and other defects, severely compromising structural safety and service life. Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency, strong subjectivity, and high costs, while conventional image processing techniques and early deep learning models (e.g., U-Net, Faster R-CNN) still perform inadequately in complex environments (e.g., varying illumination, noise, false cracks) due to poor perception of fine… More >