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

acc
 
CONTENTS
Volume 21, Number 4, April 2026
 

Abstract
This study presents a computational framework for modeling solid-color reinforced composite aggregates and concrete–steel bond strength using a physics-informed and data-driven simulation strategy. The approach integrates experimental observations with a structured model-development protocol that formalizes hypothesis testing, parameter calibration, and validation pathways to enhance reproducibility and transparency. The framework enables systematic investigation of interactions among silica-fume-modified cement matrices, rubberized aggregates, and steel reinforcement interfaces. A series of nonlinear computational models is established to predict compressive strength, tensile performance, and bond stress–slip relationships. The models are calibrated using experimental datasets and refined through iterative evaluation of competing constitutive assumptions, improving both predictive robustness and mechanistic interpretability. Results indicate that composite aggregate incorporation significantly modifies interfacial stress transfer mechanisms, leading to enhanced bond strength and ductility under optimized silica fume content. Compared with conventional empirical fitting approaches, the proposed modeling framework reduces parameter uncertainty and demonstrates improved generalization across material configurations. The methodology provides a scalable basis for the design and evaluation of sustainable composite concrete systems with improved structural reliability and interface performance.

Key Words
computational material design; concrete–steel bond strength; data-driven and physics-guided modeling; interfacial mechanics; model interpretability and reproducibility; Narrquest framework; rubberized concrete; silica fume; solid-color reinforced composite aggregates; sustainable concrete materials

Address
Ying-Chiang Cho: International Joint Institute of Tianjin University, Fuzhou, Fujian, China; School of Physics and Information Engineering, Minnan Normal University, Fujian, China

Abstract
Recent efforts in sustainable construction focus on reducing cement consumption while improving the performance of fiber-reinforced high-strength concrete (FRHSC). This study investigates the combined influence of ground granulated blast furnace slag (GGBS) and recycled steel fibers (RSF) on the fresh, mechanical, and durability properties of FRHSC, supported by machine learning-based strength prediction. Cement was partially replaced with GGBS at levels of 15% and 30%, while RSF were incorporated at 0.25%, 0.50%, and 0.75% by volume. Fresh properties were evaluated through workability-related tests. Mechanical performance was assessed using compressive strength (CS), splitting tensile strength (STS), and flexural strength (FS) tests at 7, 28, and 90 days. Durability was examined using sorptivity, water absorption, rapid chloride penetration, and electrical resistivity tests. The random forest (RF) and artificial neural network (ANN) models were developed to predict strength behavior. The results showed that GGBS improved workability and long-term strength due to its delayed pozzolanic activity and formation of secondary C-S-H gel, leading to a denser microstructure. RSF enhanced tensile and FS by bridging cracks and limiting crack propagation. At 90 days, the paste comprising 30% GGBS and 0.75% RSF achieved increases of 13.61% in CS, 35.73% in STS, and 31.2% in FS compared to the reference mix. Durability performance was significantly enhanced, with up to a 66.38% reduction in chloride ion penetration, attributed to pore refinement and reduced permeability. SEM analysis confirmed a compact and homogeneous microstructure in GGBS-fibermodified concrete. Among the machine learning models, the RF approach demonstrated superior predictive accuracy compared to ANN, with higher R2 values and lower prediction errors. The findings highlight the synergistic role of GGBS and recycled RSF in producing durable and high-performance concrete. The integration of machine learning further provides an efficient tool for strength prediction, reducing experimental dependency. This study offers practical guidance for developing sustainable FRHSC with enhanced mechanical performance, durability, and predictive reliability.

Key Words
ANN; chloride penetration; durability; GGBS; machine learning; strength

Address
Nabil Ben Kahla: Department of Civil Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61411, Saudi Arabia; Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia
Nejib Ghazouani: Mining Research Center, Northern Border University, Arar 73222, Saudi Arabia
Umara Nasir: Department of Civil Engineering, University of Engineering and Technology Taxila, 47050, Pakistan

Abstract
This paper presents a novel approach to reducing cement consumption in concrete by using a combination of nano-silica (NS) and nano-calcium carbonate (NC) as cementitious materials-reducing admixtures (CRA). The innovation lies in the synergistic effect of NS and NC, which not only reduce cement content by 20% but also enhance concrete's mechanical properties and durability. Through a systematic optimization, the combination of 1.751% NS and 0.1% NC was found to significantly improve concrete performance compared to the control mix. Specifically, the 28-day compressive strength increased by 7.9%, the splitting tensile strength rose by 11.8%, and the chloride-ion permeability resistance decreased by 2.7%. Additionally, the optimized mix reduced the concrete cost by 11.17 CNY/ton and decreased CO2 emissions by 27.4 kg/ton. This study provides strong evidence for the practical application of NS and NC in reducing carbon emissions in concrete production, offering an environmentally friendly and economically viable alternative to traditional cement-based mixtures.

Key Words
mechanical and durability performance; mix proportion optimization; nano-calcium carbonate; nano-silica; response surface methodology

Address
Xue Lijia: The 9th Engineering Co. Ltd., Mbec, Cuiheng New District, Zhongshan, Guangdong, China

Abstract
The adoption of innovative monitoring technology is of greater importance than ever as infrastructure sustainability and safety take precedence. While ultra-high-performance cementitious composites (UHPCC) provide superior performance compared to normal-strength concrete (NSC) and high-performance concrete (HPC), their inherent brittleness may render them insufficient, making structures susceptible to abrupt breakdowns. Compounding this issue, conventional monitoring methods are frequently unable to deliver precise data, which results in a critical shortfall in the ability to assess structural integrity. Therefore, this review focuses on reinforcing UHPCC with macrosynthetic fibers as a potent strategy for a more resilient structural material and examines the practical implications of fiber-optic technology for structural monitoring. Studies have demonstrated that adding merely 1% macro-synthetic fibers can lead to a dramatic change in the flexural behavior that evolves from an over-reinforced compression mode to a pseudo-ductile flexural response, emphasizing the material

Key Words
fiber-matrix interaction; fiber-optic systems; macro-scale; synthetic reinforcement; ultra-highstrength cementitious materials

Address
Stephanie Yen Nee Kew, Jacob Lim Lok Guan: Department of Civil Engineering, Faculty of Engineering and Built Environment, National University of Malaysia, 43600 Bangi, Selangor, Malaysia

Abstract
Most ancient city walls in China are severely damaged mainly because of the destruction of traditional pure white mortar in the walls. Traditional mortar and cement paste are typically used for restoring ancient city walls; however, traditional mortar exhibits low strength, excessive shrinkage, poor durability, and poor compatibility between the cement paste and the original mortar. Therefore, there is an urgent need to develop new repair materials for ancient building masonry. A new type of modified pure white mortar was developed in this study. Hydroxypropyl methylcellulose and calcium stearate were added to pure white mortar, and the working performance, basic mechanical properties, and shrinkage of the modified pure white mortar were investigated. The microstructures of the modified mortar were analysed using X-ray diffraction and scanning electron microscopy techniques. A 0.4% HPMC or 0.1% CS content can significantly improve the performance of the traditional mortar, and the modified mortar is suitable for ancient masonry repairs.

Key Words
ancient building masonry; calcium stearate; experimental research; hydroxypropyl methylcellulose; micro-analysis; mortar

Address
Pang Chen, Zepeng Zhang: Department of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
Zaixian Chen, Song Wu, Shengpeng Hu: Department of Civil Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China

Abstract
Tall reinforced concrete buildings are increasingly becoming popular due to the rising housing demand and scarcity of space. These buildings need specific consideration with respect to the design and detailing. Most importantly, the dynamic behaviour and seismic damage of these buildings are considerably different compared to low- and mid-rise structures. Damaged or seismically deficient buildings can be restored with a suitable strengthening strategy, however, a pre-assessment of the lateral load behaviour of these buildings is necessary. Presently, seismic strengthening measures based on nonlinear analysis are abundant, and each of them is used according to the need or the damage level incurred in the structure or its components. However, seismic strengthening based on visible cracks or damage in the building, followed by a linear analysis to assess its performance level is also in practice. The present study is motivated towards the seismic assessment and rehabilitation of one such case study 15-storied reinforced concrete building that was hit and damaged by the M7.8 Gorkha earthquake on 25 April 2015 in Nepal. Optimal strengthening strategies for the building are suggested based on the overall improvement in its seismic performance and damage probability through nonlinear analyses.

Key Words
damage assessment; nonlinear analysis; RC building; seismic performance; strengthening

Address
Abdul H. Tramboo, Trishna Choudhury: Department of Civil Engineering, Thapar Institute of Engineering and Technology,
Patiala, 147004, Punjab, India

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