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Advanced Technologies for Construction and Maintenance of Engineering Structures

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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5892

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Special Issue Editors

Prof. Dr. Zhongqiu Fu

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Guest Editor

College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: steel bridges; intelligent construction; intelligent maintenance; fatigue performance improvement

Prof. Dr. Fangwen Wu

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Guest Editor

School of Highway, Chang’an University, Xi’an 710064, China
Interests: steel–concrete composite structure; carbon-reinforced concrete composite structure; high-performance materials; seismic resistance
Special Issues, Collections and Topics in MDPI journals

Dr. Qiudong Wang

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Guest Editor

College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: fatigue strengthening; shape memory alloy; fatigue damage evaluation; fatigue life prediction; FRP composites
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced technologies are revolutionizing the lifecycle management of engineering structures, driving unprecedented gains in efficiency, safety, durability, and sustainability. This Special Issue will focus on the transformative role of cutting-edge innovations—including robotics, artificial intelligence (AI), machine learning (ML), computer vision, the Internet of Things (IoT), 5G connectivity, advanced sensors, building information modeling (BIM), digital twins, drones, and computational methods—in automating and enhancing both the construction and maintenance of civil infrastructure.

We welcome submissions on the following topics:

Robotics and automation;
AI/ML and data analytics;
Smart sensing;
Digital twins and BIM;
Advanced computational methods;
Remote inspection and monitoring;
Intelligent construction systems;
Data-driven methods;
Resilience and sustainability.

Prof. Dr. Zhongqiu Fu
Prof. Dr. Fangwen Wu
Dr. Qiudong Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent construction
intelligent maintenance
experimental techniques
modeling methods
machine learning
engineering structures

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Published Papers (8 papers)

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Research

27 pages, 21390 KB

Open AccessArticle

Investigation on the Dynamic Response and Failure Mode of Clay Brick Masonry Walls Under Long-Duration Explosion

by Chengrui Wang, Kai Zhang, Wei Liu, Peng Li, Ming Yang and Xiaolei Chen

Buildings 2026, 16(10), 2011; https://doi.org/10.3390/buildings16102011 - 20 May 2026

Abstract

Masonry structures are widely used in civil engineering due to their favorable load-bearing capacity and construction efficiency; however, the threat posed by long-duration blast loads from industrial accidents and large-yield explosions has become increasingly prominent. Existing research has primarily focused on the response of masonry walls under conventional short-duration explosions, while systematic investigations remain limited regarding the differentiated failure mechanisms induced by long-duration blasts. To address this gap, this study adopts and validates a full-scale simplified micro-modeling approach for clay brick masonry walls using LS-DYNA. The model enables systematic comparison of long-duration blast loads and conventional blast loads simulated by the CONWEP method under equal peak overpressure and equal impulse conditions. Numerical results indicate that, under equal peak overpressure (0.18 MPa), the long-duration blast load induces global deformation and cumulative damage leading to complete collapse, whereas the conventional blast load results in only elastic response. Under equal impulse (13.5 kPa·s), both loads cause severe damage, yet the conventional blast load triggers instantaneous localized fragmentation with a higher collapse rate, while the long-duration blast load governs failure through sustained overpressure-induced global deformation and crack propagation. The comparison of mid-span displacement–time histories across different loading cases further quantifies these distinct failure modes, revealing fundamentally different deformation development rates and collapse characteristics. The key contributions of this study are summarized as follows: A validated simplified micro-model is developed that reproduces the experimental damage patterns of masonry walls. A comparison identifies and mechanistically explains the differentiated failure modes between the two load types. Under the conditions considered in this study, critical transition thresholds of peak overpressure and impulse governing the damage mode shift from minor cracking to global collapse are determined. These findings provide a scientific basis for distinguishing blast-resistant design strategies for masonry structures according to explosion type. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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19 pages, 4314 KB

Open AccessArticle

Digital Image-Based Deformation Measurement Method for LNG Modular Transport Beam–Column Joints

by Jian Yang, Gang Shen, Yuxi Huang, Yu Fu, Juan Su, Peng Sun and Xiaomeng Hou

Buildings 2026, 16(6), 1125; https://doi.org/10.3390/buildings16061125 - 12 Mar 2026

Viewed by 363

Abstract

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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13 pages, 3912 KB

Open AccessArticle

Influences of Inclination Angles and Loading Scenarios on the Elasto-Plastic Stability of a Steel Basket-Handle Arch Structure

by Zijing Zhang, Zhanfei Wang, Qiang Zhang and Jia Chen

Buildings 2026, 16(5), 1013; https://doi.org/10.3390/buildings16051013 - 4 Mar 2026

Viewed by 340

Abstract

This study investigates the effects of an arch rib inclination angle and loading scenario on the elasto-plastic stability of steel basket-handle arches to support bridge design. A parametric finite element analysis was performed on 48 models, with inclination angles ranging from 0° to 15° under three vertical loading conditions: uniformly distributed (V), transversely eccentric (V₁), and longitudinally eccentric (V₂). A nonlinear analysis was conducted using the arc-length method. The results indicate that the ultimate bearing capacity is highest under loading V, followed by V₁ and V₂, irrespective of the inclination angle. The initial stiffness increases monotonically with inclination in all cases. Under V, the capacity peaks at a 10° inclination before declining, with a corresponding transition from out-of-plane to in-plane buckling at this critical angle. Under V₁, out-of-plane buckling dominates, and the capacity fluctuates slightly before increasing with the inclination. Under V₂, in-plane antisymmetric buckling prevails, and the capacity decreases gradually as the inclination increases. Eccentric loading induces severe stress concentration and local buckling at the arch feet, accelerating global failure. It is concluded that an inclination angle up to 10° enhances elasto-plastic stability under symmetric vertical loading, whereas eccentric loading substantially reduces the capacity; therefore, symmetric and simultaneous loading on both arches is recommended during construction. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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28 pages, 3362 KB

Open AccessArticle

Application of Multi-Ribbed Composite Wall Structure in Rural Housing: Seismic, Carbon Emissions, and Cost Analyses

by Yanhua Wu, Yue Wang, Haining Wang, Meng Cong, Hong Zhang, Francis Deng Clement, Yiming Xiang and Chun Liu

Buildings 2026, 16(2), 465; https://doi.org/10.3390/buildings16020465 - 22 Jan 2026

Viewed by 477

Abstract

Sustainable development is crucial worldwide. Under the Paris Agreement, countries commit to Nationally Determined Contributions (NDCs) assessed every five years. China, a major contributor to global warming, has made significant efforts to reduce carbon emissions and achieve carbon neutrality, a key strategy for sustainable development. However, there is a lack of adequate attention to embodied emission reduction in rural residential construction, despite a surge in building to improve living standards. This paper evaluated the feasibility of applying a multi-ribbed composite wall structure (MRCWS) in rural China through a village service project. A full-scale shaking table test was conducted to study its seismic performance. Carbon emissions were analyzed using process-based life cycle assessment (P-LCA) and the emission-factor approach (EFA), while costs were estimated using life cycle costing (LCC) and the direct cost method (DCM). These analyses focused on sub-projects and specific structural members to validate the superiority of this prefabricated structure over common brick masonry. MRCWS blocks were prefabricated by mixing wheat straw with aerocrete, utilizing agricultural by-products from local farmlands, thus reducing both construction-related carbon emissions and agricultural waste treatment costs. Results show that this novel precast masonry structure exhibits strong seismic resistance, complying with fortification limitations. Its application can reduce embodied carbon emissions and costs by approximately 6% and 10%, respectively, during materialization phases compared to common brick masonry. This new prefabricated building product has significant potential for reducing carbon emissions and costs in rural housing construction while meeting seismic requirements. The recycling of agricultural waste highlights its adaptability, especially in rural areas. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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23 pages, 4505 KB

Open AccessArticle

Research on the Seismic Mitigation and Reinforcement Control Effect Based on the Development of Low-Frequency Viscoelastic Damping Materials

by Teng Ge, Chao Xu, Jia-Xuan He, Zhong-Wei Hu and Zhongqiu Fu

Buildings 2026, 16(1), 30; https://doi.org/10.3390/buildings16010030 - 21 Dec 2025

Viewed by 539

Abstract

Viscoelastic dampers (VEDs) in seismic structures comprehensively enhance the dynamic performance of the structure by dissipating energy, providing additional stiffness and damping. The optimization analysis of dampers is the core link to ensure the safety, economy, and effectiveness of seismic design schemes. This work aims to develop low-frequency high-performance viscoelastic damping materials (VEMs) and verify the seismic control effect through three-dimensional solid engineering structure analysis. Four different damping systems of Acrylate Rubber (ACM) based viscoelastic materials were fabricated and performance characterization tests were conducted. The results indicate that all four damping modification systems can significantly improve the energy dissipation capacity of viscoelastic damping materials at low-frequency room temperature. The viscoelastic damping material with the best comprehensive performance has been selected and applied to the viscoelastic dampers of the three-dimensional shock-absorbing structure. Through the analysis of the structural vibration control effect, the universality of the vibration control effect of ACM-based viscoelastic materials under different seismic loads was further verified. It provides a feasible approach for the trans-scale research of “Material–Device–Structure” in viscoelastic damping technology. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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22 pages, 1678 KB

Open AccessArticle

Image Completion Network Considering Global and Local Information

by Yubo Liu, Ke Chen and Alan Penn

Buildings 2025, 15(20), 3746; https://doi.org/10.3390/buildings15203746 - 17 Oct 2025

Viewed by 915

Abstract

Accurate depth image inpainting in complex urban environments remains a critical challenge due to occlusions, reflections, and sensor limitations, which often result in significant data loss. We propose a hybrid deep learning framework that explicitly combines local and global modelling through Convolutional Neural Networks (CNNs) and Transformer modules. The model employs a multi-branch parallel architecture, where the CNN branch captures fine-grained local textures and edges, while the Transformer branch models global semantic structures and long-range dependencies. We introduce an optimized attention mechanism, Agent Attention, which differs from existing efficient/linear attention methods by using learnable proxy tokens tailored for urban scene categories (e.g., façades, sky, ground). A content-guided dynamic fusion module adaptively combines multi-scale features to enhance structural alignment and texture recovery. The frame-work is trained with a composite loss function incorporating pixel accuracy, perceptual similarity, adversarial realism, and structural consistency. Extensive experiments on the Paris StreetView dataset demonstrate that the proposed method achieves state-of-the-art performance, outperforming existing approaches in PSNR, SSIM, and LPIPS metrics. The study highlights the potential of multi-scale modeling for urban depth inpainting and discusses challenges in real-world deployment, ethical considerations, and future directions for multimodal integration. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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34 pages, 8392 KB

Open AccessArticle

Shear Behavior of Large Keyed Dry Joints in Segmental Precast Bridges: Experiment, Numerical Modeling, and Capacity Prediction

by Yongjun Hou, Duo Liu, Di Qi, Song Liu, Tongwei Wang and Jiandong Zhang

Buildings 2025, 15(18), 3375; https://doi.org/10.3390/buildings15183375 - 17 Sep 2025

Cited by 2 | Viewed by 1296

Abstract

The mechanical properties of the joint are a key factor influencing the overall structural performance of segmental precast beams. This study investigates the shear performance of large keyed dry joints in segmental precast beam specimens under six different conditions, including variations in the base height of the key, depth-to-height ratio, number of keys, and prestressing reinforcement ratio, using direct shear tests and numerical simulations. The mechanical performance of the joints in segmental precast bridges under combined bending and shear forces is also studied using finite element analysis software. Additionally, a prediction model for the shear strength of the large keyed dry joints is established using machine learning methods. The results show that increasing the base height, depth-to-height ratio, and overall dimensions of the key can enhance the shear strength of dry joints. The depth-to-height ratio of the key not only affects the shear strength of the dry joint but also determines the failure mode of the joint. Furthermore, the shear bearing capacity and displacement stiffness of the keyed dry joint increase with the reinforcement ratio of the prestressing tendons. Compared to smaller keyed joints, larger keyed dry joints exhibit higher shear bearing capacity, smaller relative slip at failure, and a simpler casting process, making them more suitable for application in segmental precast bridges. The influence of bending moment on the shear bearing capacity of the joint section is limited, with the relative variation compared to the pure shear condition being less than 10%. The shear bearing capacity of the joint section in segmental precast bridges can be designed based on its direct shear performance. The developed interface shear strength prediction model effectively captures the nonlinear relationship between various parameters and shear strength, demonstrating strong adaptability and accuracy. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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27 pages, 3349 KB

Open AccessArticle

Prediction of the Shear Strengths of New–Old Interfaces of Concrete Based on Data-Driven Methods Through Machine Learning

by Yongqian Wu, Wantao Xu, Juanjuan Chen, Jie Liu and Fangwen Wu

Buildings 2025, 15(17), 3137; https://doi.org/10.3390/buildings15173137 - 1 Sep 2025

Viewed by 1208

Abstract

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

(This article belongs to the Special Issue Advanced Technologies for Construction and Maintenance of Engineering Structures)

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