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CONTENTS
Volume 37, Number 3, March 2026
 

Abstract
The study aimed to forecast asphalt concrete's dynamic modulus (|E∗|, |G∗|) using hybrid machine learning, combining MLP (Multi-Layer Perceptron), SVM (Support Vector Machine), DT (Decision Tree), RF (Random Forest), LR (Logistic Regression), and AdaBoost classifiers to understand dataset complexities. With 2,238 data points from 2010 to 2023 and diverse asphalt concrete samples, a 70-30 train-testing (70% for training and 30% for testing) split ensured thorough analysis. This dataset's diversity was further enriched by incorporating asphalt concrete samples with varying geometries of 100 mmx200 mm and 100 mmx150 mm (diameterxheight), contributing to a more holistic understanding of the material's behavior. Evaluation metrics included confusion matrix, MAE, RMSE, and R2. Results highlighted predictive models' impact on |E∗| and |G∗| values, especially MLP's accuracy (0.964) and precision (0.955), making it reliable for engineers and researchers. MLP also excelled in the testing dataset (accuracy: 0.964, precision: 0.903). SVM followed with 0.880 accuracy and 0.852 precision. These outcomes reinforced the MLP model's reliability and underscored its potential as an asset in predicting |E∗| and |G∗| modulus values, affirming its practical applicability in geotechnical studies and research endeavors.

Key Words
asphalt concrete; dynamic modulus; hybrid machine learning; material characterization; predictive modeling

Address
Liu Wei: 1) Jiangxi University of Science & Technology, Ganzhou 341000, Jiangxi, China, 2) Gannan University of Science & Technology, Ganzhou 341000, Jiangxi, China
Lu Xinrong, Chen Liang, Deng Daping: Gannan University of Science & Technology, Ganzhou 341000, Jiangxi, China
Mehdi Kouhdarag: Civil Engineering Department, Malekan Branch, Islamic Azad University, Malekan 5561788389, Iran
Liang Tongxiang: Jiangxi University of Science & Technology, Ganzhou 341000, Jiangxi, China

Abstract
Given the application constraints of ultra-high performance concrete (UHPC) due to high cost and raw material scarcity, along with adverse environmental effects from iron tailings accumulation, this paper presents economically feasible and eco-friendly UHPC (e-UHPC) by completely replacing quartz sand with iron tailings. This study aims at boosting the efficient utilization of iron tailings and improving the applicability of UHPC materials in practices. The effects of mono steel fibers and hybrid steel and polyoxymethylene (POM) fibers on the tensile properties of e-UHPC were investigated through direct tension tests with various fiber type and content. The results showed that both steel and hybrid fiber-reinforced e-UHPC can exhibit high post-cracking load-bearing capacity and obvious strain hardening behavior with sensitive cracking patterns depending on fiber contents. Steel fibers significantly improved the strength of e-UHPC but barely impacted on its deformability, whereas higher POM fiber content in hybrid specimens apparently enhanced the strain capacity of e-UHPC without improving its strength. Based on tensile stress-strain responses of test specimens, a multi-parameter tensile constitutive model for e-UHPC with varying fiber contents was established, enabling the accurate estimation of e-UHPC under direct tension.

Key Words
iron tailings; polyoxymethylene (POM) fibers; tensile behavior; tensile constitutive model; ultra-high performance concrete (UHPC)

Address
Rui Zhang: College of Mining Engineering, North China University of Science and Technology (NCUST), Tangshan, China
Jianwei Chen, Zhanwen Wang, Wei Zhang: College of Civil and Architectural Engineering, NCUST, Tangshan, China
Deuckhang Lee: School of Civil and Architectural Engineering and Department of Global Smart City, Sungkyunkwan University, Suwon, Korea

Abstract
Carbonation of hardened cement paste is one of the main damages causes in the corrosion of steel reinforcement, which may endanger the concrete structure, leading to deterioration and loss their integrity. For this purpose, the carbonation depth of concrete should be predicted. The current study investigates to predict the impact of partially replacing Portland cement with limestone filler on the carbonation depth of concrete by a deep learning algorithm. Therefore, an optimizer algorithm (Adam) and a Huber loss function were used to train this model. The developed model demonstrated excellent predictive performance, achieving a coefficient of determination (R2) exceeding 98%, a root mean square error (RMSE) of 1.81, and a mean absolute error (MAE) of 1.25. Therefore, a parametric analysis was performed to study the impact of the main factors that influence this phenomenon. Finally, a deep learning model, such as a Convolutional Neural Network (CNN), was created, and a parametric study was performed. The results demonstrated that the utilization of CNN drastically enhanced the accuracy of the model, lending it a high level of validity as a reliable tool for accurately simulating and predicting the carbonation depth of concrete. Accordingly, the proposed model is capable of predicting carbonation-induced corrosion and can serve as a fundamental tool for predicting the service-life of concrete structures.

Key Words
carbonation depth; concrete; convolutional neural networks; limestone filler

Address
Geomaterials Laboratory, Department of Civil Engineering, University Hassiba Benbouali, P.O.Box 151, Chlef 02000, Algeria

Abstract
This study aims to determine appropriate intensity measures (IMs) used in linear probabilistic seismic demand models (PSDMs) for single-story precast RC industrial buildings. To this end, a methodology based on correlation, efficiency, practicality, proficiency, and sufficiency criteria is presented for selecting optimal IMs. The 29 IMs commonly used in seismic vulnerability and risk assessment are categorized as acceleration-related, velocity-related, displacement-related, and compound. The non-linear analyses were performed by applying the Incremental Dynamic Analysis (IDA) method to 50 earthquake ground motion records compatible with the target spectrum. Park and Ang Damage Index (DI(P&A)) and Maximum Drift Ratio (MDR) were used as damage parameters to measure the structure response. Numerous PSDMs were developed and a large number of regression analyses between damage parameters and IMs were performed. Following detailed analysis and evaluation, Housner Intensity (HI) and Velocity Spectrum Intensity (VSI) were identified as the optimum IM parameters. Finally, fragility curves were developed as a function of appropriate IMs that are considered to give reasonable results in addition to the optimum IMs.

Key Words
damage index; fragility curve; optimum intensity measure parameters; precast building; probabilistic seismic demand models

Address
Department of Earthquake Engineering, Disaster Management Institute, Istanbul Technical University, 34469, Istanbul, Türkiye

Abstract
In this study, a lateral strength predictive model of CFST columns under lateral cyclic loading was developed using machine learning (ML) algorithms. A total of 82 experimental datasets on the CFST columns under axial and lateral loading collected from the literature served as the training and testing data to build the predictive model. An extreme Gradient Boosted Tree (ExGBT) was utilized to interpret the trained ML models. To demonstrate the efficiency of such a model, the ExGBT performance was compared with other ML techniques, such as Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Decision Trees (DT). The overall correlation coefficient was 0.951, 0.989, 0.994, and 0.995 for MLR, ANN, DT, and ExGBT, respectively, indicating that the lateral strength of the CFST column can be well-predicted by all considered methods. However, ExGBT was the optimum algorithm for lateral strength prediction of the CFST column with the lowest MAPE of 15.32%.

Key Words
artificial neural network (ANN); concrete-filled steel tube; cyclic loading; decision tree (DT); extreme gradient boosted tree (ExGBT); maximum lateral strength; multiple linear regression (MLR)

Address
Mai-Suong T. Nguyen: 1) Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, Republic of Korea, 2) Thuyloi University, Ha Noi, Viet Nam
Seung-Eock Kim: Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, Republic of Korea

Abstract
This research investigates the experimental, optimization and prediction of the mechanical properties of normal and self-compacting concrete by examining varying ratios of gravel, basalt, and dolomite aggregates. Each mix consists of one or two different types of aggregates combined in varying proportions to assess their influence on concrete performance. The study aims to evaluate how these different aggregate compositions influence key mechanical properties, including compressive strength, tensile strength and flexural strength. Experimental tests were conducted on concrete samples with varying proportions of the aggregates, followed by a comprehensive analysis of their performance. The results indicate that self-compacting concrete generally outperforms normal concrete in mechanical properties, with dolomite contributing most to compressive strength, basalt excelling in flexural and tensile strengths, and gravel showing the weakest performance across all cases. Genetic Algorithm (GA), were employed to develop predictive models for the mechanical properties of both normal and self-compacting concrete based on the aggregate ratios. The formula developed through GA demonstrates practical applicability in assessing the mechanical properties of both normal and self-compacting concrete, while accounting for varying ratios of gravel, basalt, and dolomite aggregates. This research provides valuable insights for engineers and researchers in the field of construction materials, contributing to the development of more efficient and sustainable concrete solutions.

Key Words
basalt; concrete mechanics properties; dolomite; genetic algorithm; gravel; material performance; normal concrete; self-compacting concrete

Address
Alaa Atwa: Department of Structural Engineering, Menoufia University, Menoufia, Egypt
Youssef Lattif: Department of Structural Engineering, Zagazig University, Zagazig, Egypt
Osman Hamdy: Department of Civil Engineering, Zagazig Higher Institute of Engineering & Technology, Zagazig, Egypt

Abstract
Functionally graded materials (FGMs) constitute a category of advanced composites characterized by a continuous variation of mechanical and thermal properties, resulting from gradual changes in composition or microstructure. This gradation enables the design of components with tailored performance for specific engineering applications. In this study, an elasticity-based analytical solution is developed for the static analysis of a functionally graded cantilever beam under a cubic load distribution. In contrast to most existing studies that primarily address uniformly distributed or simple load cases, the present work introduces a new analytical framework capable of capturing the response of graded beams under higher-order distributed loading. The formulation uses the Airy stress function to predict the internal stress fields within the beam, ensuring that both equilibrium and compatibility conditions are rigorously satisfied. By applying the boundary conditions consistent with the cantilever configuration, closed-form expressions for the stress components and deflection are derived. The obtained results show the capability of the proposed approach to accurately capture the stress distribution and deformation response of functionally graded beams under cubic loading. This highlights not only the efficiency of the method but also its novelty in extending elasticity-based solutions beyond conventional load assumptions, providing new insights for analyzing graded structures under higher-order load distributions.

Key Words
boundary conditions; cubic loads; elasticity solution; functionally graded beam; static analysis

Address
Mohamed Nassah: Laboratory of Geomatics and Sustainable Development, University of Tiaret, Algeria
Lazreg Hadji: Department of Civil Engineering, University of Tiaret, Algeria

Abstract
This study was originally presented at the 18th World Conference on Earthquake Engineering (WCEE2024). Observations from recent earthquakes indicate that modern reinforced concrete (RC) buildings located in near-fault regions have experienced substantial damage, partly attributable to design standards that were primarily developed based on far-fault ground motion characteristics. Consequently, enhancing the seismic resilience of such structures against near-fault ground motions has become an urgent engineering priority. Ultra-high performance concrete (UHPC), recognized for its superior mechanical properties, offers a promising solution for strengthening existing RC structures. This study investigates the structural performance of RC T-beams strengthened in the negative moment region using UHPC overlays. A three-dimensional finite element (FE) model was developed in ABAQUS to simulate the nonlinear behavior of concrete and steel reinforcement, incorporating 13 mm and 16 mm steel rebars. The numerical model was validated against six full-scale experimental tests, demonstrating high accuracy in predicting load capacity, displacement response, and failure modes. An analytical approach was also proposed to estimate the flexural capacity of the strengthened beams. Furthermore, a comprehensive parametric study was conducted to evaluate the effects of both near-fault and far-fault ground motions on structural response. The results show that UHPC strengthening enhances load-carrying capacity under near-fault excitation by up to 115%, accompanied by a significant reduction in tensile damage in the flange region, while also providing notable capacity improvements under far-fault conditions. The findings confirm the effectiveness of the proposed FE framework and highlight the potential of UHPC overlays in improving the seismic resilience of RC T-beams, particularly in near-fault regions.

Key Words
finite element analysis; near fault ground motion; strengthening; T-beam; ultra-high performance concrete

Address
Yanuar Haryanto: 1) Department of Civil Engineering, Faculty of Engineering, Jenderal Soedirman University, Jln. Mayjen. Sungkono KM 5, Blater, Purbalingga 53372, Indonesia, 2) National Center for Research on Earthquake Engineering, No. 200 Sec. 3, Xinhai Road, Taipei 10668, Taiwan, 3) Department of Civil Engineering, College of Engineering, National Cheng Kung University, No. 1 University Road, Tainan 701, Taiwan
Hsuan-Teh Hu, Laurencius Nugroho, Ming-Hang Wu: Department of Civil Engineering, College of Engineering, National Cheng Kung University, No. 1 University Road, Tainan 701, Taiwan
Fu-Pei Hsiao, Pu-Wen Weng, Chia-Chen Lin: National Center for Research on Earthquake Engineering, No. 200 Sec. 3, Xinhai Road, Taipei 10668, Taiwan

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