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 >

Yingui Qiu¹, Enming Li^1,2,*, Pablo Segarra², Bin Xi³, Jian Zhou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1607-1629, 2025, DOI:10.32604/cmes.2025.068211 - 31 August 2025

Abstract With the growing demand for sustainable development in the mining industry, cemented paste backfill (CPB) materials, primarily composed of tailings, play a crucial role in mine backfilling and underground support systems. To enhance the mechanical properties of CPB materials, fiber reinforcement technology has gradually gained attention, though challenges remain in predicting its performance. This study develops a hybrid model based on the adaptive equilibrium optimizer (adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB. Through systematic comparison with various other machine learning methods, results demonstrate that the proposed hybrid model exhibits… More >

Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960 - 16 April 2024

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables,… More > Graphic Abstract

Yinhuan Yang^1,*, Manfeng Gong¹, Xiaoqun Xia¹, Yuling Tang²

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1239-1249, 2021, DOI:10.32604/cmes.2021.014129 - 19 February 2021

Abstract In order to study the mechanical properties of Z-pins reinforced laminated composite single-lap adhesively bonded joint under un-directional static tensile load, damage failure analysis of the joint was carried out by means of test and numerical simulation. The failure mode and mechanism of the joint were analyzed by tensile failure experiments. According to the experimental results, the joint exhibits mixed failure, and the ultimate failure is Z-pins pulling out of the adherend. In order to study the failure mechanism of the joint, the finite element method is used to predict the failure strength. The numerical… More >

A. Ataş¹, C. Soutis²

CMC-Computers, Materials & Continua, Vol.34, No.3, pp. 199-226, 2013, DOI:10.3970/cmc.2013.034.199

Abstract A strength prediction method is presented for double-lap single fastener bolted joints of cross-ply carbon fibre reinforced plastic (CFRP) composite laminates using cohesive zone elements (CZEs). Three-dimensional finite element models were developed and CZEs were inserted into subcritical damage planes identified from X-ray radiographs. The method makes a compromise between the experimental correlation factors (dependant on lay-up, stacking sequence and joint geometry) and three material properties (fracture energy, interlaminar strength and nonlinear shear stress-strain response). Strength of the joints was determined from the predicted load-displacement curves considering sub-laminate and plylevel scaling effects. The predictions are More >