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CONTENTS
Volume 20, Number 4, October 2025
 

Abstract
The interaction between reinforced concrete (RC) frames and infill masonry walls plays a critical role in the seismic performance of structures. Post-earthquake observations have shown that buildings with masonry-infilled RC frames often suffer significant damage due to the excessive stiffness of infill walls rigidly connected to the main frame. This study examines the influence of infill masonry walls on the lateral strength, stiffness, and energy dissipation capacity of RC frames through numerical analysis of scaled models. Three configurations are considered: a bare RC frame, an RC frame with masonry infill, and an RC frame with masonry infill incorporating expanded polystyrene as an energy-dissipating layer. The RC frame model is calibrated and validated using experimental data and serves as the reference for analysing the other two models. Pushover and cyclic loading tests are carried out to assess the seismic response of the infilled frame, with particular attention of the effect of energy-dissipating materials.

Key Words
ductility; energy dissipating; infill masonry; RC frames; seismic performance; strength

Address
National Earthquake engineering Research Centre (CGS), 01 Rue Kaddour Rahim Prolongée BP 252, Hussein Dey, Algeria.

Abstract
The bubble deck slab system reduces self-weight of slab without reducing strength. In this study, singly reinforced bubble deck concrete slabs were created by incorporating hollow plastic spheres to form voids beneath the neutral axis. The bubble deck slabs, with varying plastic ball percentages (0%, 10%, 20%, 30%, and 40%), were subjected to fire exposure at temperatures of 250°C, 500°C, and 750°C for durations of 1 and 2 hours. Their load-bearing capacity was then evaluated under single-point flexure. The slabs were tested to simulate real-world fire scenarios and assess how heat affects structural integrity. The performance comparison under static loading conditions, with and without fire exposure, helps evaluate how varying void percentages in bubble deck slabs affect their structural behavior and resilience under different conditions. This study also explored the load-deflection behavior, as well as the impact of different temperature levels and exposure durations on the slabs' overall performance, offering a comprehensive evaluation of their durability and load-bearing capacity under fire conditions.

Key Words
fire exposure; load deflection curve; plastic balls; static loading; void percentage

Address
Department of Civil Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India.

Abstract
The mechanical characteristics of concrete, encompassing essential factors such as Compressive Strength, Deflection, Bond Strength, and Shear Strength, are of paramount significance in the design of structurally sound Reinforced Concrete elements. Notably, the assessment of beam deflection is particularly critical for serviceability concerns; however, its accurate determination often requires rigorous analysis or destructive testing. Predicting these vital parameters based on the available test data holds immense value for the design community. Traditional empirical and statistical models, including linear and non-linear regression, have long been employed for this purpose. However, they can be time consuming and prone to errors owing to the variability in factors such as reinforcement, concrete properties, design mix ratios, and curing conditions. To overcome these complexities, researchers have proposed the utilization of Machine Learning algorithms, such as Artificial Neural Networks, Support Vector Machines, Decision Trees, Random Forest, for the prediction of mechanical properties in Reinforced Concrete Beams. The input datasets employed to train these models encompass a range of critical variables, including cement, mineral admixture, Coarse Aggregate, Water/Cement Ratio, Fine Aggregate, Superplasticizer, Slump Value, concrete age, Reinforcement, Reinforcement spacing, Reinforcement, Reinforcement Tensile strength, number of Reinforcement, and Concrete Cover. This study conducts a comprehensive assessment of the applicability and performance of each model, yielding practical recommendations, identifying current knowledge gaps, and delineating potential directions for future research endeavors in this domain.

Key Words
age of concrete; concrete compressive strength; machine learning; prediction; reinforcement

Address
(1) Saurabh Dubey, Mainak Mallik:
Department of Civil Engineering, National Institute of Technology Arunachal Pradesh, Jote 791113, India;
(2) Deepak Gupta:
Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj 211004, India;
(3) Barenya Bikash Hazarika:
Faculty of Computer Technology, Assam down town University, Sankar Madhab Path, Panikhaiti, Guwahati, Assam 781026, India.

Abstract
This study investigates the mechanical performance of eco-friendly non-proprietary ultra-high-performance fiberreinforced concrete (UHPC) using locally available cementitious materials and evaluates the effect of ambient and thermal curing regimes over short- and long-term durations. Key mechanical properties, including compressive strength, first-peak flexural strength, and modulus of elasticity (MOE), were experimentally assessed. Results demonstrated that thermal curing significantly accelerated early-age strength development, with short-term compressive and flexural strength enhancements ranging from 77% to 90%, and long-term gains of 10% to 30% compared to ambient curing. MOE exhibited consistent growth across both curing methods, achieving values up to 52 GPa. Predictive equations for MOE and first-peak flexural strength were established to aid in design applications. Furthermore, two machine learning models—Random Forest (RF) and k-Nearest Neighbors (KNN)—were employed to predict mechanical performance. The RF model outperformed KNN across all metrics, achieving correlation coefficients (R2) between 0.93 and 0.99 and minimal error values (RMSE < 1.21 for compressive strength). These findings validate the potential of non-proprietary UHPC as a sustainable alternative to commercial mixes and provide a predictive framework for its behavior under varying curing conditions, advancing its practical implementation in structural applications.

Key Words
ambient curing; local materials; Machine Learning (ML); mechanical properties; thermal curing; Ultra-High-Performance Concrete (UHPC)

Address
(1) Turki S. Alahmari:
Department of Civil Engineering, Faculty of Engineering, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia;
(2) Brad D. Weldon:
Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA.

Abstract
This research develops a higher-order vibrational model for graphene origami-reinforced composite cylindrical panels operating in thermal environments. The formulation incorporates foldability-dependent constitutive relations through modified micromechanical coefficients that capture temperature-induced property transitions. Foldability concept is used for the graphene origami in which transforms graphene from a 2D material into a reconfigurable 3D platform, merging nanoscale precision with macroscale functionality. The core innovation leverages graphene's geometric reconfiguration capability—transforming 2D sheets into programmable 3D architectures to achieve synergetic nanoscale precision and macrostructure functionality. Kinematic relations in cylindrical coordinates integrate higher-order bending, transverse shear, and thickness stretching functions. Governing equations derive from Hamilton's principle, explicitly incorporating pre—stress from thermoelectro- magnetic loads. Vibration suppression correlates with increased crease density and thermal exposure, while origami concentration enhances damping capacity. A diminish in vibration responses is detected with an increase in foldability parameter and thermal loads. An enhanced output is detected with an increase in the origami content and decrease in the folding parameter.

Key Words
cylindrical panel; foldability; higher-order modelling; multi-field loading; stretching ability; vibrational-based formulation

Address
(1) Rahadian Zainul:
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, Padang, Indonesia;
(2) Rahadian Zainul:
Center for Advanced Material Processing, Artificial Intelligence, and Biophysics Informatics (CAMPBIOTICS), Universitas Negeri Padang, Indonesia;
(3) Rahadian Zainul, Mohanad Hatem Shadhar:
Researcher Fellow at Asia Pacific University of Technology and Innovations (APU), Malaysia;
(4) Y.M. Kadhim:
Department of Civil Engineering, College of Engineering, Al-Iraqia University, Baghdad, Iraq;
(5) C. Manjunath:
Department of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India;
(6) Raman Kumar:
Department of Mechanical Engineering, Rayat Bahra University, Kharar, Punjab 140103, India;
(7) Raman Kumar:
Faculty of Engineering, Sohar University, PO Box 44, Sohar, PCI 311, Oman;
(8) Raghda Ali Bakr:
Department of Medical Laboratory Technics, College of Health and Medical Technology, Alnoor University, Mosul, Iraq;
(9) Ahmed Elawady:
College of Technical Engineering, The Islamic University, Najaf, Iraq;
(10) Mohd Abul Hasan, Saiful Islam:
Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.

Abstract
This research advances a sophisticated continuum framework for analyzing cylindrical shells enhanced by auxetic (negative Poisson's ratio) metamaterial cores. A unique kinematic description, specifically formulated to capture non-uniform strain distributions through the shell's thickness, replaces conventional shear deformation theories. The analyzed composite system integrates a metallic phase (copper) incorporating architectured, three-dimensional graphene-origami reinforcements. The effective macroscopic properties of this complex material are determined via an empirically-informed micromechanical homogenization scheme, enabling the rigorous definition of constitutive behavior within the shell's intrinsic curvilinear geometry. Governing equations describing the coupled structural response are systematically derived through an energy-based variational principle. A comprehensive numerical investigation then explores the influence of critical design parameters on the shell's mechanical performance under quasi-static transverse loading. Key variables examined include the geometric folding characteristics of the graphene-origami reinforcement, its volumetric concentration within the matrix, and the effect of uniform and non-uniform thermal field environments. Results quantitatively illustrate the significant impact of these factors on loaddisplacement characteristics, stress redistribution, and the overall structural efficiency of the auxetic-reinforced shell system.

Key Words
auxetic metamaterial; folding characteristic; graphene origami; shear deformable; three-dimensional reinforcement; variational-based formulation

Address
(1) Mohanad Hatem Shadhar, Yasir W. Abduljaleel:
Department of Civil Engineering, College of Engineering, Al-Iraqia University, Baghdad, Iraq;
(2) Zaid A. Mohammed:
Al-Bayan University, Technical College of Engineering, Department of Medical Instrument Technical Engineering, Iraq;
(3) Juan José Flores Fiallos, Lenin Santiago Orozco Cantos, Víctor Miguel Toalombo Vargas:
Universidad Nacional de Chimborazo, Riobamba, Chimborazo ‌‎060106, Ecuador;
(4) Navin Kedia:
NIMS School of Civil Engineering, NIMS University Rajasthan, Jaipur, India;
(5) Muhannad Riyadh Alasiri, Saiful Islam:
Civil Engineering Department, College of Engineering, King Khalid University, Abha 61421,Saudi Arabia.

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