Yidong Xu¹, Weijie Zhuge^1,2, Jialei Wang¹, Xiaopeng Yu^3,*, Kan Wu⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1507-1527, 2025, DOI:10.32604/sdhm.2025.069021 - 17 November 2025

Abstract The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the More >

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025

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 >

Nadia Gouider¹, Mohammed Benzerara^2,*, Yazid Hadidane¹, S. M. Anas^3,*, Oulfa Harrat¹, Hamda Guedaoura^2,4, Anfel Chaima Hadidane⁵, Messaoud Saidani⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 457-481, 2025, DOI:10.32604/cmes.2025.071600 - 30 October 2025

Abstract In recent years, cold-formed steel (CFS) built-up sections have gained a lot of attention in construction. This is mainly because of their structural efficiency and the design advantages they offer. They provide better load-bearing strength and show greater resistance to elastic instability. This study looks at both experimental and numerical analysis of built-up CFS columns. The columns were formed by joining two C-sections in different ways: back-to-back, face-to-face, and box arrangements. Each type was tested with different slenderness ratios. For the experiments, the back-to-back and box sections were connected using two rows of rivets. The… More > Graphic Abstract

Haoyun Fan¹, Soon Poh Yap^1,*, Shengkang Zhang¹, Ahmed El-Shafie^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 17-65, 2025, DOI:10.32604/cmes.2025.070880 - 30 October 2025

Abstract Data-driven research on recycled aggregate concrete (RAC) has long faced the challenge of lacking a unified testing standard dataset, hindering accurate model evaluation and trust in predictive outcomes. This paper reviews critical parameters influencing mechanical properties in 35 RAC studies, compiles four datasets encompassing these parameters, and compiles the performance and key findings of 77 published data-driven models. Baseline capability tests are conducted on the nine most used models. The paper also outlines advanced methodological frameworks for future RAC research, examining the principles and challenges of physics-informed neural networks (PINNs) and generative adversarial networks (GANs), More >

Xing Lin¹, Junning Wu¹, Shixue Liang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 215-239, 2025, DOI:10.32604/cmes.2025.070846 - 30 October 2025

Abstract Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures. This study establishes a Conditional Generative Adversarial Network (CGAN) combined with random field modeling for the efficient prediction of stochastic crack patterns and stress-strain responses. A total dataset of 500 samples, including crack propagation images and corresponding stress-strain curves, is generated via random Finite Element Method (FEM) simulations. This dataset is then partitioned into 400 training and 100 testing samples. The model demonstrates robust performance with Intersection over Union (IoU) scores of 0.8438 and 0.8155 on training and testing datasets, More >

Yan Zhou, Hengyang Wu^*

Journal on Artificial Intelligence, Vol.7, pp. 381-396, 2025, DOI:10.32604/jai.2025.071674 - 20 October 2025

Abstract Crack Detection is crucial for ensuring the safety and durability of buildings. With the advancement of deep learning, crack detection has increasingly adopted convolutional neural network (CNN)-based approaches, achieving remarkable progress. However, current deep learning methods frequently encounter issues such as high computational complexity, inadequate real-time performance, and low accuracy. This paper proposes a novel model to improve the performance of concrete crack detection. Firstly, the You Only Look Once (YOLOv11) backbone replaces the original Real-Time Detection Transformer (RTDETR) backbone, reducing computational complexity and model size. Additionally, the Dynamic Snake Convolution (DSConv) has been introduced More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Meenakshi S. Patil^1,*, Rajesh B. Ghongade², Hemant B. Dhonde³

Journal on Artificial Intelligence, Vol.7, pp. 289-300, 2025, DOI:10.32604/jai.2025.069500 - 12 September 2025

Abstract This study seeks to establish a novel, semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes, significantly contributing to the dependability of concrete quality evaluations. The study utilizes image processing and machine learning (ML) methods, namely object detection models such as YOLOv8 and Convolutional Neural Networks (CNNs), to evaluate images of concrete cubes. These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions. Preliminary results indicate a good performance in the classification of concrete More >

Ala’a R. Al-Shamasneh¹, Faten Khalid Karim², Arsalan Mahmoodzadeh^3,*, Abdulaziz Alghamdi⁴, Abdullah Alqahtani⁵, Shtwai Alsubai⁵, Abed Alanazi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1573-1606, 2025, DOI:10.32604/cmes.2025.068887 - 31 August 2025

Abstract The fracture energy of fiber-reinforced concrete (FRC) affects the durability and structural performance of concrete elements. Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy, as the process remains time-intensive and costly. Therefore, machine learning techniques have emerged as powerful alternatives. This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC. For this purpose, 500 data points, including 8 input parameters that affect the fracture energy of FRC, are collected from three-point bending tests and employed to train and evaluate the machine… More >

Phuong Phan-Vu^*, Thanh Q. Nguyen, Phuoc Trong Nguyen

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1111-1125, 2025, DOI:10.32604/sdhm.2025.070179 - 05 September 2025

Abstract As prestressed concrete (PC) structures age, long-term effects, e.g., creep, shrinkage, and prestress losses, compromise their structural performance. Strengthening these aged PC beams has become a crucial matter. One effective solution is to use externally bonded fiber-reinforced polymer (FRP) sheets; however, limited research has been done on aged PC beams using the FRP, especially for beams with unbonded prestressing strands (UPC beams). Therefore, this research investigates the flexural strengthening efficacy of external FRP sheets on aged UPC beams with unbonded tendons. Aging minimally affected the failure modes of UPC beams, with nonstrengthened beams showing flexural… More >