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Advanced Technologies for Construction and Maintenance of Engineering Structures

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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

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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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

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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

Viewed by 259

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 532

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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