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16 pages, 7385 KB

Open AccessArticle

Temperature Field and Gradient Effects for Concrete-Filled Steel Tubular Truss Arch Bridges Under Construction

by Shijie Song, Ji Qian and Linqiang Zhou

Buildings 2026, 16(5), 969; https://doi.org/10.3390/buildings16050969 - 1 Mar 2026

Viewed by 179

Abstract

Long-span concrete-filled steel tubular truss arch bridges are extremely sensitive to thermal effects during cantilever construction, with non-uniform temperature distributions arising from mutual shading between members. The current standard JTG/T D65-06—2015 employs a simple gradient model that struggles to capture the temperature gradient [...] Read more.

Long-span concrete-filled steel tubular truss arch bridges are extremely sensitive to thermal effects during cantilever construction, with non-uniform temperature distributions arising from mutual shading between members. The current standard JTG/T D65-06—2015 employs a simple gradient model that struggles to capture the temperature gradient characteristics of complex spatial trusses, failing to meet the demands of high-precision construction. Based on a truss-type steel arch bridge in Yunnan, a thermal conduction analysis framework is proposed to calculate the temperature field of the arch rib truss and its effects, and is validated by long-term monitoring data. The results indicate that the maximum temperature difference between the upper and lower chord tubes reaches 14.53 °C, significantly changing the secondary stress distribution. There is a significant negative correlation mechanism between arch rib elevation and solar radiation temperature, necessitating consideration of solar radiation temperature effects during arch rib assembly and closure. This study establishes an analytical method for the thermal effects of long-span steel truss arch ribs, laying the foundation for arch rib profile control and stress analysis. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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14 pages, 3376 KB

Open AccessArticle

Assessing the Safety and Seismic Performance of Existing Masonry Buildings Under Overall Inclination

by Zhian Jiao, Liangfu Ma, Hanghang Liu, Yufei Guo and Dan Xu

Buildings 2026, 16(3), 533; https://doi.org/10.3390/buildings16030533 - 28 Jan 2026

Viewed by 302

Abstract

The purpose of this study was to evaluate the potential safety hazards pertaining to the overall inclination of existing masonry structures. Taking a six-story masonry residential building in Tongling as the research subject, we established a systematic safety assessment framework. Through structural entity [...] Read more.

The purpose of this study was to evaluate the potential safety hazards pertaining to the overall inclination of existing masonry structures. Taking a six-story masonry residential building in Tongling as the research subject, we established a systematic safety assessment framework. Through structural entity testing, settlement monitoring, and geological surveys, uneven foundation settlement was identified as the primary cause of the building’s inclination. A finite element model was established via SAP2000 (Version 14) software to examine and verify the bearing capacity and seismic performance of the inclined structure, and a 1:4 scale shaking table test was designed to validate the seismic performance of the structure following inclination correction. The findings indicate that the primary bearing capacity of the building’s superstructure complied with the relevant code provisions. Time–history analysis under rare six-degree earthquake conditions showed that the maximum inter-story drift angle—defined as the ratio of the maximum inter-floor horizontal displacement to the floor height under the action of the standard seismic value—was 1/2018, which is lower than the limit value of 1/900 specified for the “moderate damage” performance level of masonry structures. During the shaking table test, the natural vibration frequency of the structure remained unchanged under earthquake actions ranging from frequent to rare six-degree events, with no visible cracks or significant damage detected. This assessment system provides a technical reference for the safety performance evaluation and subsequent inclination correction of similar inclined masonry structures. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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19 pages, 1703 KB

Open AccessArticle

Element Modal-Based Structural Damage Detection by Two-Dimensional Convolutional Neural Networks

by Fuzhou Qi, Shuai Teng, Shaodi Wang, Yinghou He and Zongchao Liu

Buildings 2025, 15(21), 3905; https://doi.org/10.3390/buildings15213905 - 28 Oct 2025

Viewed by 955

Abstract

Convolutional neural networks (CNNs) have strong noise resistance, and this study utilizes this property to weaken the impact of noise on structural damage identification data. After structural damage occurs, the modal parameters at the unit level are particularly sensitive to changes in damage [...] Read more.

Convolutional neural networks (CNNs) have strong noise resistance, and this study utilizes this property to weaken the impact of noise on structural damage identification data. After structural damage occurs, the modal parameters at the unit level are particularly sensitive to changes in damage and can therefore be used as important characteristic indicators for identifying damage. This article establishes a finite element model of steel truss and introduces damage at different positions and degrees. The free vibration process of the structure is simulated by the finite element method (FEM), and the first-order modal characteristic parameters, including modal strain energy and modal strain, are extracted for each damage situation. Subsequently, these modal parameters and the corresponding damage information are input as training samples into the CNN model for automatic identification of structural damage. The results show that the constructed CNN model can accurately identify the location and degree of structural damage, with a damage localization accuracy of 100% and a relative error of only 6.6% for damage degree identification. Among various characteristic indicators, modal strain energy difference exhibits better sensitivity and stability. Compared with traditional backpropagation (BP) neural networks, the CNN shows improved detection accuracy, by about 35%, and computation time is only 2.4% of BP networks. In addition, the CNN maintains good recognition performance in low order modes, which is of great significance for easily obtainable measurement data in practical engineering. In summary, the CNN method shows superior performance in damage localization, damage degree recognition, and noise resistance and has high engineering application value. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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32 pages, 7296 KB

Open AccessArticle

Analytic Solutions for the Stationary Seismic Response of Three-Dimensional Structures with a Tuned Mass-Inerter Damper and Bracket

by Lin Deng, Cong Yao and Xinguang Ge

Buildings 2025, 15(14), 2483; https://doi.org/10.3390/buildings15142483 - 15 Jul 2025

Viewed by 662

Abstract

The ultimate goal of research on seismic mitigation technologies is engineering application. However, current studies primarily focus on the application of dampers in planar structures, while actual engineering structures are three-dimensional (3D) in nature. A type of damper, making up tuned mass dampers [...] Read more.

The ultimate goal of research on seismic mitigation technologies is engineering application. However, current studies primarily focus on the application of dampers in planar structures, while actual engineering structures are three-dimensional (3D) in nature. A type of damper, making up tuned mass dampers (TMDs) and inerters, has excellent vibration mitigation performance and needs brackets to connect to structures. In this work, a coupled dynamic model of an energy dissipation system (EDS) comprising a TMD, an inerter, a bracket, and a 3D building structure is presented, along with analytical solutions for stochastic seismic responses. The main work is as follows. Firstly, based on D’Alembert’s dynamics principle, the seismic dynamic equations of an EDS considering a realistic damper and a 3D structure are formulated. The general dynamic equations governing the bidirectional horizontal motion of the EDS are further derived using the dynamic finite element technique. Secondly, analytical expressions for spectral moments and variances of seismic responses are obtained. Finally, four numerical examples are presented to investigate the following: (1) verification of the proposed response solutions, showing that the calculation time of the proposed method is approximately 1/500 of that of the traditional method; (2) examination of spatial effects in 3D structures under unidirectional excitation, revealing that structural seismic responses in the direction along the earthquake ground motion is approximately 10⁴ times that in the direction perpendicular to the ground motion; (3) investigation of the spatial dynamic characteristics of a 3D structure subjected to unidirectional seismic excitation, showing that the bracket parameters significantly affect the damping effects on an EDS; and (4) application of the optimization method for the damper’s parameters that considers system dynamic reliability and different weights of the damper’s parameters as constraints, indicating that the most economical damping parameters can achieve a reduction in displacement spectral moments by 30–50%. The proposed response solutions and parameter optimization technique provide an effective approach for evaluating stochastic seismic responses and optimizing damper parameters in large-scale and complex structures. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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18 pages, 2431 KB

Open AccessArticle

A Dynamic Interaction Analysis of a Straddle Monorail Train and Steel–Concrete Composite Bridge

by Zhiyong Yao, Zongchao Liu and Zilin Zhong

Buildings 2025, 15(13), 2333; https://doi.org/10.3390/buildings15132333 - 3 Jul 2025

Cited by 1 | Viewed by 879

Abstract

Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance [...] Read more.

Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance of the bridge and train. It explores the influence of track irregularities and passenger loads on the dynamic response of train–bridge systems at various traveling speeds. The numerical results indicate that there is no significant resonance between the straddle monorail train and the steel–concrete composite bridge. However, track irregularities and train speed significantly amplify the responses of the train and bridge, including displacement, acceleration, and impact coefficient. Additionally, increased passenger load leads to a substantial rise in the vertical displacement of the bridge while reducing the vibration of the train, thereby improving riding comfort. The findings of this study provide valuable scientific insights and have significant practical applications for the use of steel–concrete composite bridges in straddle monorail systems. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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20 pages, 3212 KB

Open AccessArticle

Unsupervised Restoration of Underwater Structural Crack Images via Physics-Constrained Image Translation and Multi-Scale Feature Retention

by Xianfeng Zeng, Wenji Ai, Zongchao Liu and Xianling Wang

Buildings 2025, 15(13), 2150; https://doi.org/10.3390/buildings15132150 - 20 Jun 2025

Viewed by 978

Abstract

Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method [...] Read more.

Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method combines a physical modeling of underwater light transmission with a deep image translation architecture that operates without requiring paired training samples. To address the loss of fine structural details, this paper incorporates a multi-scale feature integration module and a region-focused discriminator that jointly guide the enhancement process. Moreover, a physics-guided loss formulation is designed to promote optical consistency and texture fidelity during training. The proposed approach is validated on a real-world dataset collected from submerged structures under varying turbidity and illumination levels. Both objective evaluations and visual results show substantial improvements over baseline models, with better preservation of crack boundaries and overall visual quality. This work provides a robust solution for preprocessing underwater imagery in structural inspection tasks. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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16 pages, 4163 KB

Open AccessArticle

Experimental and Theoretical Investigation on Cracking Behavior and Influencing Factors of Steel-Reinforced Concrete Deep Beams

by Gaoxing Hu, Lei Zeng, Buqing Chen and Shuai Teng

Buildings 2025, 15(11), 1812; https://doi.org/10.3390/buildings15111812 - 25 May 2025

Viewed by 1133

Abstract

Steel-reinforced concrete (SRC) deep beams have been widely used in engineering applications such as high-rise buildings and long-span bridges, with their structural behavior and mechanical properties attracting significant research attention. To investigate the shear cracking behavior of SRC deep beams, seven specimens with [...] Read more.

Steel-reinforced concrete (SRC) deep beams have been widely used in engineering applications such as high-rise buildings and long-span bridges, with their structural behavior and mechanical properties attracting significant research attention. To investigate the shear cracking behavior of SRC deep beams, seven specimens with a scale of 0.4 times were designed for static loading tests, and the influence of the shear-span-to-depth ratio λ, the width ratio of the steel flange, and the height ratio of the steel web on the width and spacing of the diagonal crack was considered. The cracking behavior of the diagonal cracks in the shear span area were recorded by the digital image correlation (DIC) technique. The results show the following: (1) the use of the DIC technology revealed the entire process of crack occurrence, development, and evolution and obtained the distribution characteristics of crack development; (2) the steel flange width has a slight effect on the spacing and width of the diagonal cracks. The diagonal crack width increased with the improvement of the height of the steel web, but the influence of the steel web on the spacing of diagonal cracks was not significant. When the height ratio increased from 0.3 to 0.45 and 0.6, the maximum oblique crack width increased by 13% and 14.5%. Based on the above experimental results and relevant analysis conclusions, an improved method was proposed to calculate the diagonal crack width of composite deep beams by further considering the influence of the crack angle. Finally, the experimental results verified its high accuracy in a qualitative analysis. The calculation method proposed in this article can be used to predict and estimate the width of diagonal cracks in SRC deep beams. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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Review

Jump to: Research

22 pages, 3412 KB

Open AccessReview

Review of Health Monitoring and Intelligent Fault Diagnosis for High-Strength Bolts: Failure Mechanisms, Multi-Modal Sensing, and Data-Driven Approaches

by Yingjie Wang, Guanghui Chu, Zhifang Sun, Fei Yang, Jun Yang, Xiaoli Sun, Yi Zhao and Shuai Teng

Buildings 2026, 16(4), 691; https://doi.org/10.3390/buildings16040691 - 7 Feb 2026

Viewed by 313

Abstract

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, [...] Read more.

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)

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