Rui Yang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010698

Abstract This study employs molecular dynamics (MD) simulations to comparatively investigate the mechanical enhancement mechanisms of glass fiber-reinforced concrete (GFRC) and basalt fiber-reinforced concrete (BFRC). Amorphous models of glass fiber (GF) and basalt fiber (BF), along with calcium silicate hydrate (C-S-H), were constructed using the ClayFF force field in LAMMPS. The interfacial transition zone (ITZ), atomic bonding characteristics, stress distribution, and tensile failure processes were systematically analyzed. Key findings reveal that BF exhibits a denser atomic network structure with higher coordination numbers, driven by the bridging role of Fe and Mg atoms. BFRC demonstrates significantly stronger More >

Lei Zheng^1,*, Hong Pan^2,3, Yuelei Ruan^2,4, Guoxin Zhang¹, Lei Zhang^1,*, Jianda Xin¹, Zhenyang Zhu¹, Jianyao Zhang^2,5, Wei Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651 - 23 December 2025

Abstract As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to More >

Yassir M. Abbas^1,*, Ammar Babiker², Fouad Ismail Ismail³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3279-3307, 2025, DOI:10.32604/cmes.2025.074069 - 23 December 2025

Abstract Accurately predicting the compressive strength of recycled aggregate concrete (RAC) incorporating supplementary cementitious materials (SCMs) remains a critical challenge due to the heterogeneous nature of recycled aggregates (RA) and the complex interactions among multiple binder constituents. This study advances the field by developing the most extensive and rigorously preprocessed database to date, which comprises 1243 RAC mixtures containing silica fume, fly ash, and ground-granulated blast-furnace slag. A hybrid, domain-informed machine-learning framework was then proposed, coupling optimized Extreme Gradient Boosting (XGBoost) with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC… More >

Siwei Zhang, Xiaozhou Xia^*, Xin Gu, Meilin Zong, Qing Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3243-3278, 2025, DOI:10.32604/cmes.2025.073841 - 23 December 2025

Abstract This study presents a coupled thermo-hydro-mechanical-fatigue (THM-F) model, developed based on variational phase-field fatigue theory, to simulate the freeze-thaw (F-T) damage process in concrete. The fracture phase-field model incorporates the F-T fatigue mechanism driven by energy dissipation during the free energy growth stage. Using microscopic inclusion theory, we derive an evolution model of pore size distribution (PSD) for concrete under F-T cycles by treating pore water as columnar inclusions. Drawing upon pore ice crystal theory, calculation models that account for concrete PSD characteristics are established to determine ice saturation, permeability coefficient, and pore pressure. To… More >

Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025

Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025

Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More > Graphic Abstract

Yassir M. Abbas^*, Abdulaziz Alsaif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1789-1819, 2025, DOI:10.32604/cmes.2025.073088 - 26 November 2025

Abstract The rapid advancement of three-dimensional printed concrete (3DPC) requires intelligent and interpretable frameworks to optimize mixture design for strength, printability, and sustainability. While machine learning (ML) models have improved predictive accuracy, their limited transparency has hindered their widespread adoption in materials engineering. To overcome this barrier, this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to model and explain the compressive strength behavior of 3DPC mixtures. Unlike conventional “black-box” models, SHAP quantifies each variable’s contribution to predictions based on cooperative game theory, which enables… More >

Sandeerah Choudhary¹, Qaisar Abbas², Tallha Akram^3,*, Irshad Qureshi⁴, Mutlaq B. Aldajani², Hammad Salahuddin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1755-1787, 2025, DOI:10.32604/cmes.2025.072200 - 26 November 2025

Abstract The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete (RAC) as an eco-friendly alternative to conventional concrete. However, predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters. This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks (FFNN), Random Forest (RF), and XGBoost. A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:… More >

Qi Feng^1,2, Weijie Hu¹, Lu Liu^3,*, Junhui Luo⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1589-1605, 2025, DOI:10.32604/sdhm.2025.072955 - 17 November 2025

Abstract Ultra-high performance fiber-reinforced concrete (UHPFRC) has received extensive attention from scholars and engineers due to its excellent mechanical properties and durability. However, there is a mutually restrictive relationship between the workability and mechanical properties of UHPFRC. Specifically, the addition of fibers will affect the workability of fresh UHPFRC, and the workability of fresh UHPFRC will also affect the dispersion and arrangement of fibers, thus significantly influencing the mechanical properties of hardened UHPFRC. This paper first analyzes the research status of UHPFRC and the relationship between its workability and mechanical properties. Subsequently, it outlines the test… More >

Weixi Zhu^1,2,3, Yongdong Meng^1,3,*, Jindong Xie², Zhenglong Cai^1,3, Yu Lyu², Xiaowei Xu^1,3

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1733-1744, 2025, DOI:10.32604/sdhm.2025.070098 - 17 November 2025

Abstract To mitigate the severe abrasion damage caused by high-velocity water flow in hydraulic engineering applications in Xizang, China, this study systematically optimized key mix design parameters, including aggregate gradation, sand ratio, fly ash content, and superplasticizer dosage. Based on the optimized mix, the combined effects of an abrasion-resistance enhancement admixture (AEA) and silica fume (SF) on the abrasion resistance of self-compacting concrete (SCC) were examined. The results demonstrated that the appropriate incorporation of AEA and SF significantly improved the abrasion resistance of SCC without compromising its workability. The proposed mix design not only achieves superior… More >