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CONTENTS
Volume 37, Number 6, June 2026
 

Abstract
Bridges are considered one of the most critical components of transportation infrastructure. Thus, an accurate response prediction of bridge structures is crucial for long-term monitoring and safety assessment. To accomplish this, the current study presents a deep learning-based approach for predicting strain responses of the I-35W Bridge at three different locations. For this purpose, the five-year measured data are adopted from this concrete box girder bridge located in Minneapolis, Minnesota. In this approach, the collected datasets are utilized as inputs to the multilevel deep neural networks, namely, deep long short-term memory (D-LSTM), deep gated recurrent unit (DGRU), and modified generative adversarial networks (GANs). The performance of these networks is also assessed using the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2) indices. The findings reveal that the deep learning-based approach is a computationally effective, promising, and reliable method for accurately predicting the future response of the bridge structure. The comparison of the final results also indicates that the proposed D-GRU model provides the best prediction accuracy and performance among the evaluated models.

Key Words
concrete bridge; D-GRU; D-LSTM; modified GANs; strain time history prediction

Address
Department of Civil Engineering, University of Kurdistan, Sanandaj, Iran

Abstract
A visco-elasto-plastic fiber bundle-chain model for creep of concrete under constant loading is developed in this work. By introducing a Kelvin-Voigt element and a dashpot chain into the fiber bundle-plastic chain model, the proposed model effectively captures both the time-dependent creep and the instantaneous plastic damage of concrete. The Kelvin-Voigt element simulates the recoverable elastic creep, while the dashpot chain characterizes the irreversible plastic creep. Furthermore, by considering the randomness of micro mechanical properties of springs and dashpots, the model conveniently characterizes the mean and stochastic creep behaviors from a microstructural perspective, avoiding the complexity of multi-factor randomness in traditional approaches. It is found that the model predictions agree well with experimental results, demonstrating its reliability. Additionally, a comparative discussion with existing models in the literature confirms the advantages of the proposed approach in simulating both deterministic and stochastic creep behavior.

Key Words
concrete; creep; damage; fiber bundle model; prediction model; visco-elasto-plasticity

Address
Zhi Shan, Zhiwu Yu: 1) School of Civil Engineering, Central South University, Changsha, China; 2) National Engineering Research Center of High-speed Railway Construction Technology, Changsha, China; 3) Engineering Technology Research Center for Prefabricated Construction Industrialization of Hunan Province, Changsha, China
Chao Huang, Hongxi Fan: School of Civil Engineering, Central South University, Changsha, China
Zhihui Zheng: School of Civil Engineering and Architecture, NingboTech University, Ningbo, China

Abstract
Although Fiber Reinforced Polymer (FRP) enhances the structural performance of concrete columns, existing predictive models for square and rectangular sections remain inadequate due to corner geometry variations and experimental uncertainties. This study aims to improve the reliability and efficiency of strength prediction for FRP-confined columns, particularly in engineering applications demanding both safety and cost-effectiveness. To address these challenges, the Group Method of Data Handling (GMDH) neural network was employed to develop a predictive model that minimizes reliance on expensive, time-consuming experiments. Several modeling approaches were examined, and the final GMDH-based neural network effectively captured the nonlinear relationships between input and output parameters, providing a robust predictive framework. Model evaluation using standard error metrics indicated strong performance, with coefficients of determination (R2) of 0.88 and 0.85 for training and testing datasets, respectively. Low error values, including Root Mean Square Error (RMSE) of 0.169 and 0.201 and Mean Absolute Error (MAE) of 0.128 and 0.157 for training and testing, confirmed the model's predictive reliability. The results demonstrate a close agreement between experimental and predicted strengths, validating the GMDH-NN as a practical, efficient alternative to extensive laboratory testing for estimating the compressive strength of FRP-confined square and rectangular columns. A significant contribution of this work is the integration of advanced neural network techniques with a comprehensive dataset of 171 specimens, offering improved insights into factors influencing strength and providing engineers with a data-driven, reliable tool for optimizing FRP-confined concrete column design.

Key Words
confinement; fiber reinforced polymer (FRP); GMDH-NN; lateral pressure

Address
Pouyan Fakharian: 1) Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; 2) School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
Bahar Mehdizadeh, Danial Jahed Armaghani: School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Hamidreza Ghazvinian, Hosein Naderpour: Faculty of Civil Engineering, Semnan University, Semnan 3513119111, Iran
Danial Rezazadeh Eidgahee: Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Biswajeet Pradhan: Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

Abstract
The extensive use of Ordinary Portland Cement (OPC) in concrete pavements has become a major concern due to its significant contribution to greenhouse gas (GHG) emissions and environmental degradation. In recent years, the use of various materials to replace and reduce clinker consumption in the production of various types of pavement concrete has expanded. Limestone Calcined Clay Cement (LC3) has gained attention as a sustainable option for reducing environmental pollutants and improving mechanical properties, with a composition of 55% OPC, 30% calcined clay, and 15% limestone powder. In this study, mechanical properties including compressive strength and flexural strength, durability against freeze-thaw cycles, relative dynamic modulus of elasticity, and environmental analysis of pavement concretes made with LC3 and OPC at binder contents of 300, 400, and 500 kg/m3 were comprehensively evaluated and compared. The results of this research showed that LC3 concretes exhibited better compressive and flexural strength at 90 days compared to OPC concretes. Additionally, the durability of LC3 samples against damage caused by freeze-thaw cycles was significantly higher, and they exhibited less reduction in the dynamic modulus of elasticity. Environmental indicators were also examined, revealing that using LC3 in concrete pavements reduced CO2 emissions by up to 36% and significantly improved the Human health, Ecosystems, and Resources indicators. Overall, the results of this study indicate that LC3 has significant potential for constructing pavements with high mechanical properties and a lower environmental impact.

Key Words
calcined clay; concrete pavement; green concrete; LC3; metakaoli

Address
Department of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran

Abstract
Full-depth precast deck panels can enhance constructability and productivity while reducing construction costs for highway bridges. This paper presents a finite element model (FEM) for direct shear tests and of precast concrete composite beams with discrete connections made with shear pockets and U-shaped steel connectors. Constitutive laws are proposed for these interfaces to accurately represent the resistance, interface slip, and strain in the U-shaped steel connectors observed during testing. Additionally, a parametric analysis of precast composite beams was conducted to investigate the influence of interface slip, shear interface stiffness, and the degree of shear connection on the flexural strength of composite beams with discrete connections. The results indicate that appropriate constitutive laws are essential for accurately representing the behavior of connections utilizing shear pockets and U-shaped steel connectors. Furthermore, parametric analysis shows that the bending resistance of precast concrete composite beams with discrete connections and low interface stiffness can be estimated using simplified analytical models, independent of the interface stiffness, with results within 15% of those obtained from finite element models.

Key Words
finite element analysis; full-depth precast slab; shear pocket; shear transfer mechanism

Address
Escola de Engenharia Civil e Ambiental, Universidade Federal de Goiás, Rua Universitária, 1488, Setor Universitário. 74605-220. Goiânia/GO, Brazil

Abstract
In this study, the optimization of concrete reinforced with iron chips, a waste product from the iron and steel industry was carried out. To this end, the Response Surface Method was used in the experimental design and empirical modelling phases. Genetic algorithm was also used to determine the optimum values. Different concrete samples with water/cement ratios of 0.40, 0.50 and 0.60 were prepared for the experimental studies. During the production of the concrete samples, waste iron chips with different shapes (flat, spiral and mixed) were added to the mix as reinforcement, based on the proportions of 0.25%, 0.50% and 0.75% of the total volume. Compressive strength and ultrasonic pulse velocity tests were performed on the concrete samples prepared in 12 series. Analysis of variance (ANOVA) was used to determine the statistical effects of the parameters on the experimental results. As a result of the study, optimum parameter levels were determined to achieve the desired properties in concrete samples reinforced with waste iron chips.

Key Words
analysis of variance; concrete; genetic algorithm; iron chips; response surface method

Address
Department of Civil Engineering, Faculty of Technology, University of Firat, Elaziğ, Türkiye

Abstract
Accurate prediction of uniaxial compressive strength (UCS) in recycled concrete aggregate (RCA) is essential for advancing sustainable construction practices. This study presents a data-driven predictive framework based on Deep Neural Networks (DNN) to estimate UCS using a comprehensive dataset comprising 326 literaturederived records and 50 experimentally validated samples. The model was trained using optimized hyperparameters over 1000 epochs and evaluated through a combination of statistical metrics and cross-validation techniques. The proposed DNN model demonstrated superior predictive performance compared to benchmark regression models, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Decision Tree (DT), achieving an accuracy of 0.925 with significantly reduced error values. The robustness of the model was further validated using independent experimental data, confirming its generalization capability in real-world conditions. The novelty of this study lies in integrating heterogeneous data sources with a rigorous validation framework, enhancing both reliability and applicability. The findings highlight the potential of deep learning techniques to support efficient material design and decision-making in recycled concrete applications, contributing to more sustainable and resource-efficient construction practices.

Key Words
compressive strength; concrete aggregate; deep learning; recycled concrete; sustainable construction

Address
Yaser A. Nanehkaran, Yuan Xiaofeng: School of Artificial Intelligence, Yancheng Teachers University, Yancheng 224002, Jiangsu, China
Tolga Pusatli: Department of Management Information Systems, Cankaya University, Ankara, Türkiye
Yimin Mao: School of Information and Engineering, Shaoguan University, Shaoguan 512005, Guangdong, China

Abstract
Concrete-filled steel tube (CFST) is considered a promising solution for enhancing the structural performance of steel structures. This paper presents the development of empirical model for predicting the ultimate moment capacity and flexural stiffness of composite beams, emphasizing the interaction between the concrete core and steel sections. A comprehensive comparison was conducted between the experimental results collected from past studies and the predicted results from existing standards and analytical methods. The existing design models reveal significant inconsistencies and inaccuracies in predicting the results. To address this issue, Grey Correlation Analysis (GCA) was conducted to address the sensitivity of the key parameters influencing the moment capacity and flexural stiffness of CFST beams. The proposed formula considers the key parameters, including confinement ratio (xi), geometric properties (t/D), materials strength (fck and fy), and the elastic modulus of materials (Es and Ec), to improve the precision and practical applicability of the empirical formula. A reliable group of experimental and numerical database were collected from past research to predict the ultimate moment, initial stiffness, and serviceability-level stiffness. Validation was conducted based on the results collected from past research to ensure the applicability and reliability of the developed empirical formula. Furthermore, the predicted value from new methods were compared with the results obtained from the published literatures. The proposed models demonstrate good agreement and better consistency with the experimental results.

Key Words
CFST beams; empirical formula; flexural stiffness; grey-correlation analysis; moment capacity

Address
Tan Wan Han, A. B. M. A. Kaish, Shahrizan Baharom, Jacob Lim Lok Guan: Department of Civil Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Ahmed W. Al Zand: Department of Design, College of Fine Arts, Alturath University, Baghdad, Iraq

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