Wu Feng^1,2,*, Tengku Anita Raja Hussin¹, Xu Yang³

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071664 - 08 January 2026

Abstract This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700°C by integrating experimental testing and advanced numerical modeling. A temperature-indexed Concrete Damage Plasticity (CDP) framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution. Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation. Results demonstrated a distinct two-stage degradation regime: moderate stiffness and strength reduction up to ~400°C, followed by sharp deterioration beyond 500°C–600°C, with residual capacity at… More >

Mahmoud Alhashash¹, Abdullah Alariyan², Ameen Mokhles Youns³, Favzi Ghreivati⁴, Ahed Habib^5,*, Maan Habib⁶

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070194 - 08 January 2026

Abstract Concrete production often relies on natural aggregates, which can lead to resource depletion and environmental harm. In addition, improper disposal of thermoplastic waste exacerbates ecological problems. Although significant attention has recently been given to recycling various waste materials into concrete, studies specifically addressing thermoplastic recycled aggregates are still trending. This underscores the need to comprehensively review existing literature, identify research trends, and recognize gaps in understanding the mechanical performance of thermoplastic-based recycled aggregate concrete. Accordingly, this review summarizes recent investigations focused on the mechanical properties of thermoplastic-based recycled aggregate concrete, emphasizing aspects such as compressive… More >

Jia Yan Lim^1,2, Siti Madiha Muhammad Amir³, Roslan Yahya³, Marta Peña Fernández², Tze Chuen Yap^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… 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 >

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 >

Ke Li¹, Bendong Liu¹, Yulong Han², Yafeng Zhang³, Chunqi Yang¹, Dawei Yin², Yazhou Zhang³, Wantao Ding^4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2779-2793, 2025, DOI:10.32604/fdmp.2025.072671 - 01 December 2025

Abstract Traditional cement-based slurries are often constrained by excessive cement consumption, prolonged setting times, and limited controllability, which hinder their broader engineering applications. To overcome these challenges, this study focuses on optimizing ordinary cement-based slurry through the incorporation of targeted additives and rational adjustment of mix proportions, with the aim of developing a rapid-setting, early-strength cementitious system. In particular, a series of comparative and orthogonal experiments were conducted to systematically examine the evolution of the slurry’s macroscopic properties. In addition, the response surface methodology (RSM) was introduced to reveal the interaction mechanisms among key parameters, thereby… More >

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 >

Yidong Xu¹, Weijie Zhuge^1,2, Jialei Wang¹, Xiaopeng Yu^3,*, Kan Wu⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1507-1527, 2025, DOI:10.32604/sdhm.2025.069021 - 17 November 2025

Abstract The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the More >