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CONTENTS
Volume 36, Number 2, August 2025
 

Abstract
The analytical model for structures that accounts for all sources of stiffness, P-delta effects, and inelastic response is the most accurate approach for seismic design. The design codes specified factor is known as the response modification factor (R), which represents the ratio between the required base shear forces to keep the structure elastic during the earthquake and the design base shear force considering its inelastic behavior. This study aims to estimate the over strength modification factor (R) for various irregularly of reinforced concrete dual systems at failure using the pushover analysis through the finite element method and compare them with those recommended by the design codes. The structures under investigation were divided into three different heights of 5, 10 and 15 stories. These structures were a dual structural system. Two types of vertical irregularities, classified according to American Society of Civil Engineers (ASCE)/SEI 7-16, have been investigated; soft story and weight (mass). The two types of vertical irregularities were located at three different locations for each type, bottom story, 1/3 from structure height, and 2/3 from structure height. It was concluded that the R coefficient values presented in the current study was not align with those derived from Egyptian or other structural codes.

Key Words
pushover analysis; soft story irregularity V1; weight (mass) irregularity V2

Address
Tarek Salah El-Salakawy, Mosaad El-Diasity and Mina Mounir Naguib: Civil Engineering Department, Faculty of Engineering at Shubra, Benha University, Benha, Qalubiya Governorate, Egypt
and Mohamed Hamdy: Civil Engineering Department, Faculty of Engineering at Badr, Badr University, Cairo, Egypt

Abstract
The fine aggregates were replaced with Recycled Brick Powder (RBP) and mixed with foam concrete. Its mechanical and physical properties were examined using an orthogonal design experiment (5 levels) with a 4-factor analysis. By conducting specific strength analysis, it becomes possible to determine the optimal ratio for foam concrete containing RBP. These studies aimed to explore how the mechanical and physical properties of foam concrete mixed with brick powder are affected by varying factors such as water-to-material ratio, Hydroxypropyl Methyl Cellulose ether (HPMC) concentration, addition of air bubble group, and addition of RBP. Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Network enhanced by Particle Swarm Optimization (PSO-BP), Both backpropagation neural networks (BP) and multiple linear regression (MLR) are used. When considering forecasting accuracy and model stability, it was discovered that the PSO-BP model surpassed the performance of the other five machine learning models. The experimental results confirm that the ideal concrete mix with 0.05% HPMC and 30% RBP with 0.55 w/c ratio provides the maximum compressive strength (5.68 MPa). The PSO-BP model fitted well with the highest regression value (R2=0.961).

Key Words
concrete mix with foam; concrete; modelling studies; recycled brick powder; strength properties

Address
Murali G: Centre for Promotion of Research, Graphic Era (Deemed to be University), Clementtown, Dehradun - 248002, India
Bharathi Murugan R: Technical Training, Afcons Infrastructure Limited, Mumbai - 400053, Maharashtra, India
Haridharan M K: Department of Civil Engineering, National Institute of Technology, Arunachal Pradesh, Jote - 791113, Arunachal Pradesh, India
Maruthi Venkatesh K: Department of Civil Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore - 641407, Tamilnadu, India

Abstract
Compressive and flexural strengths (CS and FS) constitute a vital parameter in designing various concrete structures, including rigid pavements, beams, and bridges, holding significant importance in ensuring their structural integrity and performance. The prevailing industry standard for concrete evaluation, the compressive strength test, is favored for its procedural simplicity. However, the estimation of CS and FS, especially for High-Performance Concrete (HPC), remains challenging due to material variability, mix complexity, curing conditions, and testing variations, necessitating advanced modeling approaches for accuracy. In response to this, the present research advocates the integration of Histogram Gradient Boosting (HGB) with Reptile Search Optimization (RSO), Arithmetic Optimization Algorithm (AOA), Sooty Tern Optimization Algorithm (STOA), Leader Harris hawks optimization (LHHO) and an ensemble of 4 optimizers to elevate the precision of such assessments. The results of this study reveal that among the various prediction models under examination, the HGB+RSA (HGRS) outperforms all other models, boasting the highest R2 value of 0.9914, and HGB+AOA (HGAO) is the second-best model with an R2 of 0.9846. When shifting the focus to FS estimation, the hybrid HGRS model shines with exceptional performance, displaying optimized R2 and RMSE values of 0.9922 and 0.2447, respectively. This underscores the effectiveness of the optimized Histogram Gradient Boosting with RSO in predicting CS and FS values for HPC. Interestingly, the study suggests that employing an ensemble of 4 selected optimizers developed reliable models with R2 of higher than 98% compatible with data exchange.

Key Words
ensemble learning; high-performance concrete; histogram gradient boosting; mechanical properties; optimization algorithms

Address
Ying Gao, Xiao Zhang, Yanyan Zhao and Ying Huang: Shandong Xiehe University, Jinan Shandong 250107, China
Lei Gao: Geotechnical and Structural Engineering Research Center of Shandong University, Jinan Shandong 250061, China
Zhenxing Guo: Shandong Dawei International Architecture Design Co., Ltd., Jinan Shandong 250000, China

Abstract
Multi-scale simulation is an effective approach to investigating the influence of the cement hydration process on the microstructure and macroscopic mechanical properties of concrete. However, the computational efficiency restricts its practical application in engineering. In this study, we establish an effective and accurate cement hydration model by proposing the "centripetal displacement method" for efficient placement of cement particles and the "extreme contact positioning method" for particle contact determination. By combining the cohesion model, we obtain the mechanical properties of cement paste at different stages of hydration. The simulation results of the cement hydration model and the mechanical properties, including the degree of hydration, elastic modulus, compressive strength, and tensile strength, exhibit good agreement with the experimental results for water-to-cement ratio ranging from 0.3 to 0.5. Based on the proposed model, the effects of the water-to-cement ratio and hydration age on the degree of hydration, microstructure, and mechanical properties of cement paste,motar and concrete are analyzed.

Key Words
concrete; hydration; mechanical properties; multi-scale

Address
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China

Abstract
This paper investigates the compressive, tensile, and flexural behavior of concrete produced using waste tire rubber in the form of fibers, and also estimates its capacities with artificial neural networks (ANN). This study aims to reuse waste tires in concrete production by considering environmental awareness and recycling, both of which are becoming increasingly important. Three different ratios of fiber-formed waste tire rubber, specifically 5%, 10%, and 15% by volume, were utilized. Slump value measurement, compressive strength, splitting tensile strength, and flexural tests were performed on the samples. The distribution of rubber fibers in the concrete was investigated by SEM. Thanks to the developed algorithm, ANN was performed, and the optimum design configuration was determined. When the results of the analysis were examined, it was observed that the use of waste rubber in the form of fiber increased the workability of the concrete, while it caused a decrease in the compressive strength, cylinder splitting strength, and beam bending tensile strength . As the ratio of tires used increases, the more the reduction in these strengths becomes pronounced.

Key Words
fiber composite concrete; mechanical tests; recycled tire rubber-filled concrete; rubber

Address
Ali Serdar Ecemiş and Yasin Onuralp Özkiliç: Necmettin Erbakan University, Faculty of Engineering, Department of Civil Engineering, Konya, Türkiye
Şaban Gülcü: Necmettin Erbakan University, Faculty of Engineering, Department of Computer Engineering, Konya, Türkiye
Emrah Madenci: 1) Necmettin Erbakan University, Faculty of Engineering, Department of Civil Engineering, Konya, Türkiye, 2) Department of Technical Sciences, Western Caspian University, Baku 1001, Azerbaijan
Ali İhsan Çelik: Tomarza Mustafa Akincioglu Vocational School, Department of Construction, Kayseri University, Kayseri, 38940, Türkiye


Abstract
In this study, the stress-strain curves of concrete containing rubber by partial replacement of fine aggregate under axial compression after high temperature have been researched. The percentage of rubber content is 5%, 10% and 15% by volume. 6 plain concretes (PC) and 18 rubberized concretes (RC) are heated at 100 oC, 200 oC, 400 oC, 600 oC and 800 oC.The obtained results indicate that the compressive strength and modulus of elasticity of rubber concrete decrease following the increasing temperature. Contrarily, the increase of the mass loss ratio, peak stress and specific toughness of concrete containing rubber is affected by temperature and the failure model of concretes demonstrate the more rubber, the greater the ductility. Cracks and color changes on the surface of the specimen as the temperature increases. The proposed stress-strain curves model is to predict the stress-strain relationships of rubber concretes after elevated temperature, which demonstrated a reasonable result.

Key Words
compressive performance; rubber concrete; stress-strain curves; temperature

Address
Wanjie Zou: College of Civil and Architecture Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
Hanliang Xie and Jiongfeng Liang: Faculty of Civil & Architecture Engineering, East China University of Technology, Nanchang 330013, China
Wei Li: 1) College of Civil and Architecture Engineering, Wenzhou University, Wenzhou 325035, China, 2) Key Laboratory of Engineering and Technology for Soft Soil Foundation and Tideland Reclamation of Zhejiang Province, Wenzhou 325035, China

Abstract
High-performance concrete compressive and tensile strengths are essential in terms of the assurance of structural performance and reliability. The research will describe the effective estimation of such properties through an artificial intelligence-based approach to overcome several limitations of experimental testing. For this purpose, a Light Gradient Boosting model has been developed and enhanced using four meta-heuristic optimization algorithms: Dandelion Optimization, Runge-Kutta Optimization, Seagull Optimization Algorithm, and Black Widow Optimization Algorithm. The LGRDSB was an ensemble model that combined the strengths of all four optimizers. Among them, the RUN optimizer with the LGRK model emerged as the best, giving R-squared values of 0.9928 and 0.9914 for CS and TS predictions, respectively. Thus, the LGRDSB model ensemble emerged as most robust and reliable to handle diverse datasets, securing R-squared values greater than 98% and less than 1% error rates. These results highlight the performance of the proposed models in predicting HPC properties and provide a realistic approach toward integrating AI techniques into performance evaluation for HPC.

Key Words
artificial intelligence; ensemble learning; high-performance concrete strengths; hybrid machine learning; light gradient boosting

Address
Shaoka Zhao: School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University, Fuqing 350300, China
Hongwei Li: Zhejiang Industry &Trade Vocational College, Wenzhou 325000, Zhejiang, China
Jianfeng Li: 1) Faculty of Engineering, China University of Geosciences (Wu han), Wuhan 430000, China, 2) Xing Yun Chen (Hong Kong) Technology Limited, Hong Kong 999077, China, 3) Hainan Cloud Spacetime Information Technology Co., Ltd., Danzhou 571700, China
Linbin Li: Fuzhou Immigration Management Office, Fuzhou 350005, China
Yongjun Liu: Fujian Dongchen Construction Engineering Group Co., LTD, Fuzhou 350005, China
Shuanglan Wu: College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Yongning Liang: School of Civil Engineering, Fuzhou University, Fuzhou 350108, China
Feilan Wang: School of International Business and Economics, Fujian Business University, Fujian 350012, China
Junbo Chen: 1) Zhejiang Industry &Trade Vocational College, Wenzhou 325000, Zhejiang, China, 2) Cavite State University, Indang 4100, Cavite, Philippines

Abstract
This paper presents the results of numerical analysis on reinforced concrete interior beam-column joints with headed diagonal bars, utilizing 3D nonlinear finite element analysis procedures. A fracture-plastic model incorporating a smeared fixed crack approach and crack/crush band model was employed to simulate the complex behavior of these interior joints under reversed cyclic loading. Four interior beam-column joints from the literature were modeled and validated, and their detailed seismic performance was further evaluated. In addition, new parametric studies were undertaken to explore the influence of various reinforcement details on the joints' seismic performance and cracking behavior. Based on the results presented, it was shown that the utilization of headed diagonal bars led to an improved load capacity and energy dissipation. The extent of joint deterioration in specimens with headed diagonal bars, however, varied depending on the quantity of joint stirrups provided. In addition, a successful plastic hinge relocation away from the beam-column interface was obtained in the simulated models, consistent with experimental observations. Furthermore, the results from the parametric studies revealed that removing joint stirrups led to strength degradation and shear failure modes, while increasing beam longitudinal bar diameter enhanced load capacity but also strength degradation, prompting flexure-shear dominant failure modes. Moreover, the variation in bond properties between the diagonal and straight segments of headed bars was shown to have little impact on overall seismic performance, although it was important to note that joint cracks were more uniformly distributed with bonded properties, leading to more stable strength degradation.

Key Words
beam-column joints; cracking behavior; finite element analysis; headed diagonal bars; reinforcement detailing; seismic performance

Address
Asdam Tambusay and Priyo Suprobo: Department of Civil Engineering, Sepuluh Nopember Institute of Technology, Surabaya, East Java, 60111, Indonesia
Akanshu Sharma: Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, Indiana 47907-2051, USA

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