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CONTENTS
Volume 38, Number 1, July 2026
 

Abstract
This paper presents a physics informed Bayesian design of Hybrid Fiber Reinforced Concrete. The research changes the emphasis from the prediction of strength to the prescription of mix formulation. The framework was supported by a dataset of 938 samples comprising of 602 laboratory specimens and 336 physics augmented samples. A Physics Informed Neural Network that was trained on composite mechanics constraints had R2=0.93 and a mean absolute error of 2.7 MPa at 28 days. A closed form constitutive relation of compressive behavior was also derived by the model. Bayesian calibration showed the empirical coverage of 92.8% of 95% prediction intervals. An inverse mapping which had been regularized with a cost index, then determined combinations of fibers that would give target strengths between 45 and 85 MPa and still be economically viable. 70 experimental tests of mixtures showed that the mean absolute error is 3.8 MPa which corresponds to approximately 6.5% relative error and that the mixtures saved 18 to 25% cost in case of materials compared with conventional reference designs. These results indicate that the physics-informed neural networks could assist in better mixture design and cost-effective solutions to manufacturing sustainable management and conservation of hybrid fiber reinforced concrete.

Key Words
Bayesian uncertainty; cost optimization; hybrid fiber concrete; inverse design; physics informed neural networks; prescriptive formulation

Address
Wasim Abbass: Department of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan
Muhammad Usman Zubair: Government College University (Roll No. 0572-1-24), Lahore, Pakistan
Fahid Aslam: Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Kharj, 11942, Saudi Arabia

Abstract
Concrete is widely used as the primary material for constructing rigid pavements of roads and highways due to its versatility. However, it is prone to cracking, which can result in various issues such as rutting, potholes, shoving, and surface failure. The propagation of cracks necessitates regular inspection and rehabilitation throughout the lifespan of a project. The formation and propagation of cracks are influenced by multiple uncertain factors such as material properties, geometry, weather conditions, and traffic loads. To effectively address this issue, a stochastic approach that accounts for these uncertain quantities as random variables with appropriate probability distributions is desirable. The present study addresses the problem of crack propagation in concrete pavements from a stochastic perspective using the concepts of random walk and Markov chains. A procedure for continuous site inspections is proposed to develop statistical crack propagation and orientation models. The results of this study indicate that crack orientations are not uniformly distributed, but rather, they are clustered around specific orientations following a normal distribution with an increased coefficient of variation from the origin and a shifting mean. Thus, the Fokker-Planck equation is a suitable mathematical model for predicting the probability distribution of cracks in concrete pavement surfaces. The proposed model was found to accurately predict the distribution of crack orientations.

Key Words
Brownian motion; concrete crack; Fokker-Planck equation; Markov chain; stochastic process

Address
Moussa Leblouba: Department of Civil & Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates
Mohamad Tarabin: Department of Civil Engineering, Faculty of Engineering, McMaster University, Ontario, Canada
Mostafa Zahri: Department of Mathematics, University of Sharjah, Sharjah, United Arab Emirates

Abstract
This paper proposed a numerical modeling method for constructing concrete model through the point-volume-element aggregate interference discrimination method based on sub-region retrieval. Firstly, the conventional concrete modeling method was enhanced by optimizing the interference discrimination logic between aggregates and the aggregate retrieval approach. Subsequently, a series of concrete models with varied aggregate geometric shapes, interfacial transition zone (ITZ) thicknesses, aggregate volume fractions, and aggregate gradations were generated, validating the reliability of the proposed method. Finally, the efficiency of the proposed method was validated by comparing the number of iterations and modeling time required with those of the conventional method. The results indicate that the proposed method can control the aggregate geometric shape and ITZ thickness by adjusting the number of vertices and the scaling distance. Compared to the conventional method, the proposed method significantly reduced the number of iterations and modeling time, thereby enhancing modeling efficiency. The advantage of the proposed method became more pronounced as the number of aggregates increased. When the number of generated aggregates reaches 100, the required number of iterations is reduced by 83.6%, and the modeling time is reduced by 66.2%.

Key Words
aggregate packing algorithm; aggregate shape control; computational efficiency; interfacial transition zone (ITZ); mesoscale concrete modeling

Address
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China

Abstract
This study investigates the bending behavior of hybrid sandwich plates composed of Functionally Graded (FG) face sheets and a Re-Entrant auxetic core subjected to hygro-thermomechanical loadings. Using a two-dimensional Higher-order Shear Deformation Theory (HSDT), the governing equilibrium and motion equations are derived via the principle of virtual work and Hamilton's principle. Closed-form Navier solutions are employed to evaluate transverse displacements, internal forces, and natural frequencies. The proposed mathematical model is validated against existing literature, confirming its accuracy. A parametric study examines the influence of hygrothermal loading, material gradation, sandwich configuration, and auxetic core geometry on structural response. Key findings indicate that hygrothermal loading is the dominant factor, amplifying nondimensional deflection by up to 300% and 220% for the (1-2-1) and (1-4-1) scheme configurations, respectively. The unit-cell geometric parameter H1 moderately affects deflection (0.8%-4.1%) yet significantly impacts transverse shear stress (up to 50%), while normal stress remains nearly insensitive. Transitioning from the (1-2-1) to the (1-4-1) configuration increases deflection by 34%, normal stress by 27%, and transverse shear stress by over 900%, highlighting the critical role of core thickness in shear redistribution. These results confirm the auxetic core's negative Poisson's ratio as a decisive factor in enhancing shear compliance and promoting uniform load distribution.

Key Words
bending; Hamilton's principle; HSDT; re-entrant auxetic core; sandwich structures

Address
Madiha Boussalem, Hayat Benachi: 1) Department of Civil Engineering, Faculty of Science and Technology, Abbès Laghrour University, Khenchela, Algeria; 2) Laboratory of Engineering and Sciences of Advanced Materials, Khenchela 40000, Algeria
Abderrahmane Menasria, Abdelhakim Bouhadra: 1) Department of Civil Engineering, Faculty of Science and Technology, Abbès Laghrour University, Khenchela, Algeria; 2) Material and Hydrology Laboratory, Civil Engineering Department, Faculty of Technology, University of Sidi Bel Abbes, Sidi Bel Abbès, Algeria
Abdelkader Tamrabet: Department of Civil Engineering, Faculty of Technology, University of Ferhat Abbas, Setif1, Algeria

Abstract
This study aims to develop a fire dynamics simulation (FDS) in a Pyrosim software platform for concrete beams subjected to natural gas fueled channel fires with controllable burners. The measured heat release rate was input as the fire load to the concrete beams. The air temperatures in the channel and inside concrete temperatures captured in the FDS are validated by the experimental results. It was found that the FDS and experimental differences in the maximum or average air temperatures are within 20% when the concrete beams loaded at a constant heat release rate. The inside concrete temperature close to the fire source is satisfactorily predicted by the one-dimensional heat conduction superposition from different concrete beam surfaces, and the predicted concrete temperatures bounded the experimental ones. With the validated FDS, a parametric analysis has been conducted to numerically clarify the impact of the side hole area, concrete thermal conductivity, specific heat and wind gust on the fire dynamics and concrete temperatures. The present study is developing a numerical technique to capture fire response of engineering structures.

Key Words
air temperature; fire dynamics simulation (FDS); fire; concrete beams; one-dimensional heat conduction

Address
Yanping Zhu: 1) National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Nanjing, 211189, China; 2) School of Civil Engineering, Southeast University, Nanjing, 211189, China
Ying Zhuo, Pengfei Ma, Genda Chen: Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Yi Bao: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA

Abstract
Tubular cylinders of short and thin-walled Reinforced Concrete (RC) specimens were simulated in this study using the Nonlinear Finite Element Analysis (NLFEA) with a total of thirty-two models to investigate the effect of different parameters on their structural behavior. The investigated parameters during the parametric study stage were: (i) the height-to-diameter ratio. (H/D) of (0.5, 1.0, 1.5, 2.0, 3.0, 3.5, and 4.0), and (ii) the elevated temperature (T) of (23, 250, 500, and 750) oC. Specimens were simulated and analyzed as horizontal cantilever beams, and the NLFEA assisted in determining the behavior of closed concrete tubular walls in bending and shear. However, short specimens are defined as cylinders with (H/D) ratio less than 2.0 and carry more loading before the failure occurrence. In addition, high temperatures of more than 500 oC resulted in the maximum reduction in structural performance. Increasing the (H/D) ratio of more than 2.0 resulted in more ultimate loading capacity for specimens exposed to temperatures less than 500 oC. The shear cracking behavior of the closed tube cylinders is similar in all specimens, where an inclined crack appears first with an angle of approximately 45 degrees. Finally, the investigation results reveal that the reduction in H/D and exposed temperature (<=500 oC) degraded the overall structural behavior, including the cracking propagation process, ductility, and ultimate moment, while the ultimate load-carrying capacity was increased. The shear ultimate strength for the closed tubular RC thin-walled cylinders was predicted using a newly introduced simple mathematical expression when exposed to elevated temperatures.

Key Words
cylindrical thin-walled; flexural; heat-damaged; lateral loading; NLFEA; shear

Address
Department of Civil Engineering, Faculty of Engineering, Jordan University of Science and Technology, PO Box 3030, Irbid 22110, Jordan

Abstract
The seismic performance of interior slab-column connections is a critical design concern due to their vulnerability to drift-induced punching shear, particularly in flat-plate systems without shear reinforcement. Existing design codes and analytical models show limited accuracy and considerable scatter when predicting unbalanced moment capacity and deformation limits, especially for both steel- and fiber-reinforced polymer (FRP) reinforced concrete systems. This study proposes a unified data-driven framework to predict the ultimate unbalanced moment capacity (Mu) and maximum drift ratio (Thetamax) of interior slab-column connections without shear reinforcement. A database of 128 experimental tests from 36 published studies was compiled, covering primarily steel-reinforced specimens, with FRP-reinforced connections included to assess cross-material generalizability via the normalized Reinforcement Index. A normalized Reinforcement Index (RI) was adopted to ensure consistent material representation. Transparent closed-form equations were derived using Multi-Gene Genetic Programming, while Random Forest and Least-Squares Boosting models were developed to enhance predictive accuracy. The proposed models significantly outperform current design provisions, achieving testing R2 values of 0.949 for Mu and 0.870 for Thetamax. Model interpretability analysis confirmed consistency with governing mechanical mechanisms, and graphical user interfaces were developed to support practical engineering application.

Key Words
drift ratio; ensemble learning; FRP reinforcement; machine learning; MGGP; slab-column connection; unbalanced moment

Address
Department of Civil Engineering, Engineering Faculty, Cukurova University, Adana 01330, Türkiye

Abstract
In this study, a new model was developed to predict the axial load-carrying capacity of circular concretefilled steel tube (CFST) columns externally confined with fiber-reinforced polymer (FRP). For this purpose, 227 experimental data points collected from the literature were split, with 75% for training and 25% for testing. A new equation was then derived using Gene Expression Programming (GEP). Additionally, prediction models were developed using several machine learning (ML) algorithms, including MLP (Multilayer Perceptron), KNN (KNearest Neighbors), BAG (Bootstrap Aggregating), RF (Random Forest), GBM (Gradient Boosting Machine), LightGBM (Light Gradient Boosting Machine), XGBoost (Extreme Gradient Boosting), and CatBoost (Categorical Boosting). A 10-fold cross-validation approach was employed during the grid search to identify the optimal hyperparameter combination for the ML models. The predictive performances of the proposed models were statistically evaluated and compared with existing equations in the literature. CatBoost demonstrated the best predictive performance on the test data, with a MAPE of 4.075, an RMSE of 180.509, an R2 of 0.988, and a COV of 0.059. SHAP analysis was used to evaluate the contribution of each input parameter to the prediction results.

Key Words
concrete-filled steel tube (CFST) columns; fiber-reinforced polymer (FRP); gene expression programming (GEP); machine learning (ML)

Address
Sema Alacali: Department of Civil Engineering, Yildiz Technical University, Istanbul, Türkiye
Fatih Cibuk: Department of Civil Engineering, Istanbul Medipol University, Istanbul, Türkiye

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