Techno Press
Tp_Editing System.E (TES.E)
Login Search
You logged in as...

cac
 
CONTENTS
Volume 38, Number 4, October 2026
 

Abstract
High-Performance Concrete (HPC) has found extensive application in rigid pavement systems due to its superior strength and durability, but due to its inherent brittle nature and low tensile strength, it remains a limiting factor in performance under repetition of wheel loads, impact action, and overall surface wear. The presence of Polyvinyl Alcohol (PVA) fibers, which are hydrophilic, have high tensile strength, are capable of bridging cracks, and make the fibers an excellent solution to improving the ductile and damage tolerance of HPC mixtures. This paper is a detailed experimental and data-based assessment of PVA fiber-reinforced HPC to be used in M50 pavement. Mechanical properties and abrasion resistance, Bohme wear loss, punching shear capacity, flexural fatigue life, and impact resistance are considered in concrete mixtures that contain 0-0.30% PVA fibers. SEM, XRD, and FTIR microstructural studies were used to give an insight into the mechanism of interaction between fibers and matrix and the development of hydration products. In addition, a structured full-factorial dataset (1,029 combinations) was developed based on experimentally validated parameter ranges and used to train Random Forest (RF), Support Vector Regression (SVM), and Artificial Neural Network (ANN) models for predictive analysis. In the experiment and the ML results, it was consistently found that 0.20% PVA fiber produced the best performance, with a flexural strength improvement of 18-24 percent, an abrasion loss of 30-40 percent, and a fatigue life increase of more than 2.5 times. Microstructural observations qualitatively indicate increased ITZ densification and enhanced C-S-H formation at this dosage. The Random Forest (RF) model demonstrated the highest predictive capability among the evaluated algorithms (R2=0.9994), although the structured nature of the dataset may contribute to reduced variability compared to field conditions. As shown in the study, PVA-HPC demonstrates improved mechanical performance, enhanced durability characteristics, and potential for data-driven optimization in rigid pavement applications.

Key Words
artificial neural network; characterization; high-performance concrete; PVA fiber; rigid pavements; support vector regression

Address
Karthik Selvam: Department of Civil Engineering, Christ the King Engineering College, Coimbatore-641104, India
Kavitha Ramasamy: Department of Civil Engineering, SNS College of Technology, Coimbatore-641048, India

Abstract
Geopolymer concrete exhibits superior resistance to high temperatures owing to its reduced thermal conductivity. Adding foam to fly ash Geopolymer concrete significantly improves its thermal properties. A precise prediction of temperature distribution during fires in buildings is essential for evaluating insulation effectiveness and structural performance. This research presents the development of simplified predictive charts for estimating temperature distribution in Geopolymer concrete reinforced elements when subjected to fire. Charts account for the variations in section geometry, including side inclination angles, dimensions, and thermal material properties. Transient thermal analysis was conducted using ANSYS Workbench to generate the charts, which were then validated against experimental data. The validated charts were further employed to evaluate the temperature distribution and the associated reduction in structural load-carrying capacity of Geopolymer concrete reinforced sections following fire exposure. This approach offers a practical and efficient tool for thermal assessment in fire design applications.

Key Words
angular sections; charts; foam; geopolymer; ISO 834 fire; random forest; transient thermal

Address
Civil Engineering Department, Faculty of Engineering, Modern University for Technology & Information (MTI), Mokattam, Cairo, Egypt

Abstract
This study investigates the steel reinforcement-to-concrete bond strength in normal concrete (NC), self-compacting concrete (SCC), and high-strength concrete (HSC) incorporating three coarse aggregate types: gravel, dolomite, and basalt. Fifty-four experimental mixtures were examined, including single aggregate types and binary combinations at varying proportions. Push-off bond strength tests were conducted on 150 mm cubic specimens with centrally embedded 10 mm deformed steel bars after 28 days of water curing. Results demonstrated that basalt aggregate achieved the highest bond strength across all concrete types, with improvements of 10-39% in NC, 13-29% in SCC, and 11-26% in HSC compared to gravel aggregate. Dolomite-basalt combinations exhibited superior performance among binary mixtures. HSC demonstrated the highest bond strength values, followed by SCC and NC. Genetic Algorithm (GA) and Bee Algorithm (BA) optimization techniques were employed to develop predictive formulas for bond strength estimation based on aggregate proportions. GA-optimized Formula-1 achieved the most accurate predictions with an objective function value of 14.59 and absolute differences not exceeding 8% compared to experimental results, with predictive accuracy being highest for HSC and decreasing progressively for SCC and NC. The developed formula demonstrated excellent reliability for predicting bond strength across various concrete types and aggregate combinations, providing a practical tool for structural engineering applications.

Key Words
basalt; bee algorithm; bond strength; dolomite; genetic algorithm; gravel; high-strength concrete; normal concrete; optimization; push-off test; 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
The use of external fiber-reinforced polymer (FRP) jacketing on columns presents a feasible and promising solution to address the issue of inadequate column ductility in existing substandard structures. Current regulations prohibit the use of FRP jacketing to improve ductility against seismic forces when the cross-sectional aspect ratio exceeds 1.5. This study investigates the seismic performance of full-scale substandard columns featuring extended rectangular cross-sections through both experimental and numerical methods. The results from the experimental tests indicate that FRP jacketing is effective for substandard columns, even when dealing with extended rectangular cross-sections. So, this paper discusses modeling the nonlinear behavior of CFRP-Jacketed RC columns under cyclic loading. In this model, the column element is represented by a linear elastic element. Also, a nonlinear rotational spring at the end of the column is used to simulate the nonlinear response of the substructure. The modeling parameters are calibrated to predict Bearing capacity, initial stiffness, ductility capacity, deformation capacity, and cyclic response of the substructures. The modeling strategy is validated by experimental results of retrofitted columns with a CFRP Jacket. OpenSees software framework is used to construct nonlinear models. Results show that the proposed nonlinear modeling method has been able to predict the hysteretic parameters of the subassembly relatively well.

Key Words
cross-sectional aspect ratio; fiber-reinforced polymer (FRP); nonlinear modeling; reinforced concrete columns; seismic retrofit

Address
Mahdi Adibi: 1) Department of Civil Engineering, University of Bojnord, Bojnord, Iran; 2) Center of International Scientific Studies and Collaborations, Ministry of Science Research and Technology of Iran, Tehran, Iran
Roozbeh Talebkhah: School of Civil Engineering, College of Engineering, University of Bojnord, Bojnord, Iran
Hamid Farrokh Ghatte: Civil Engineering Department, Faculty of Engineering, Antalya Bilim University, Antalya, Türkiye

Abstract
The strut-and-tie model (STM), employed for designing the disturbed stress region (D-region) of concrete structures, simplifies complex stress states into struts, ties, and nodes, enabling rational design. However, as the STM relies on the designer's subjective experience and judgment, design outcomes may vary, prompting various studies to propose different STM configurations and design processes for independent D-regions. Nonetheless, when a part of the structure constitutes a D-region or exhibits complex geometric features, defining the load and boundary conditions for such D-regions remains challenging. This study proposes a technique to isolate the D-region from an entire concrete structure modeled with planar solid finite elements, along with a method to determine the load and boundary conditions for the planar solid finite element model and STM of the isolated D-region. To this end, an OpenGL-based pre- and post-processing program integrating various numerical analysis functions, including finite element analysis, and graphic capabilities was developed and utilized. To validate the proposed method, D-regions were isolated from test specimens of a dapped-end beam and a beam-column joint, and the finite element analysis results of the isolated regions were compared with those at the same locations in the pre-isolated entire structure. Additionally, the sectional forces of the STM for two D-regions of the dapped-end beam were analyzed. The analysis results confirm that the proposed method enables the isolation of D-regions from the entire concrete structure and the rational determination of load and boundary conditions for the planar solid finite element model and STM of the isolated D-regions, making it a valuable tool for the STM design of non-independent D-regions.

Key Words
concrete structure; D-region; isolation; strut-and-tie model

Address
School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, Republic of Korea

Abstract
With the increasing use of concrete in road pavements, a comprehensive understanding of its fracture behavior under complex loading conditions is essential. Since aggregate gradation plays a key role in stress transfer and crack propagation, this study investigates the effect of different gravel-to-sand (G/S) ratios (0.7, 1.0, and 1.3) on the mechanical properties and fracture behavior of concrete. Fracture performance was evaluated using edge-notched diametrically compressed (ENDC) specimens under pure mode I, pure mode III, and mixed-mode I/III loading, while compressive strength, splitting tensile strength, and elastic modulus were measured on cylindrical specimens. The Results revealed that the fracture load (Pcr) and effective stress intensity factor (Keff) were lowest under pure mode I and highest under pure mode III loading. The optimal fracture performance was observed at a G/S ratio of 1. Increasing the gravel content by 3-10% relative to sand further enhanced fracture resistance. Moreover, fracture work (Wf) and fracture energy (Gf) analyses demonstrated that concrete exhibits higher sensitivity to tensile (mode I) loading than to tearing (mode III) loading, with fracture energy increasing by up to 67.9% under mode I and only 23.2% under mode III with varying G/S ratios. Mechanical testing further revealed that increasing the G/S ratio enhanced compressive strength and elastic modulus, while tensile strength decreased. These results suggest that fracture performance is not directly determined by conventional mechanical properties, highlighting the importance of considering aggregate proportioning when designing concrete for fracture-critical applications. Overall, the findings provide a quantitative basis for optimizing aggregate ratios to improve fracture resistance under different loading conditions.

Key Words
concrete; fracture energy; fracture toughness; gravel-to-sand ratio; mixed-mode I/III

Address
Ali Jahanbani, Majid R. Ayatollahi: Fatigue and Fracture Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
Siyab H. Khanghahi, Mehdi J. Barough: Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Daneshgah Street, Ardabil 56199-11367, Iran
Sadjad Pirmohammad: Fatigue and Fracture Research Laboratory, Department of Mechanical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Daneshgah Street, Ardabil 56199-11367, Iran

Abstract
The axial compression experiment on 18 columns was performed to investigate the axial behavior of CFRP-confined lithium slag concrete-filled steel tube (LSCFST) columns, considering the lithium slag (LS) substitution rate, layer number and net spacing of CFRP strips as parameters. Typical failure mode, bearing capacity, initial stiffness and ductility index were reported during the experiment. It can be found in this paper that the higher axial load and stiffness of columns are caused by more lithium slag content, more layers, and smaller spacing of CFRP strips, but the ductility index of columns drops with the rising lithium slag content. And the finite element (FE) model of CFRP strips confined columns is established and verified by experiment results. Parameter analysis shows that the optimal lithium slag content for the highest axial load of partially wrapped columns is 20%. Finally, the axial capacity calculation model proposed by Tao et al. gives the best estimation in comparison with other methods.

Key Words
axial compression; CFRP; finite element; lithium slag

Address
Liuhaoxiang Wang: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, China
Jiongfeng Liang: Faculty of Civil & Architecture Engineering, East China University of Technology, Nanchang, China
Wanjie Zou: College of Civil and Architecture Engineering, Guangxi University of Science and Technology, Liuzhou, China
Wei Li: 1) College of Civil and Architecture Engineering, Wenzhou University, Wenzhou, China; 2) Key Laboratory of Engineering and Technology for Soft Soil Foundation and Tideland Reclamation of Zhejiang Province, Wenzhou, China

Abstract
In this study, the reliability of machine learning (ML) models for predicting the shear capacity of reinforced concrete (RC) beams exposed to corrosion was investigated. A dataset involving 204 experimental results collected from the literature was constructed for this investigation. The prediction performance of ML models was evaluated for the training and testing datasets. The mean value (MV), standard deviation (STD), root mean square error (RMSE), coefficient of determination (R2), and coefficient of variation (COV) were utilized to compare the prediction performance of ML models. It was concluded this comparison that overfitting and underfitting problems were overcome in the predictive performance of ML models. Furthermore, another conclusion from the ML model's prediction results was that the predictions obtained using ML models were accurate and robust according to the statistical indicators mentioned above. The new equation, which was obtained with the help of the Gene Expression Programming (GEP) model, was proposed. Moreover, Extreme Gradient Boosting (XGBoost) was selected as the best model out of eleven ML models. The MV, STD, RMSE, R2 and COV values of the XGBoost model for testing dataset are 1.028, 0.116, 13.725, 0.965 and 0.113, respectively. The prediction results of the XGBoost was superior to those of the five analytical models available in the literature. Finally, SHapley Additive Explanations (SHAP) analysis was performed on the XGBoost model.

Key Words
corrosion; extreme gradient boosting (XGBoost); gene expression programming (GEP); machine learning (ML); shapley additive explanations (SHAP)

Address
Hasan Cem Akkaya: Department of Civil Engineering, Kirklareli University, 39000, Kirklareli, Türkiye
Sema Alacali: Department of Civil Engineering, Yildiz Technical University, 34220, Istanbul, Türkiye

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2026 Techno-Press ALL RIGHTS RESERVED.
P.O. Box 33, Yuseong, Daejeon 34186 Korea.
General Inquiries: info@techno-press.com / Journal Administration: admin@techno-press.com