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 >

Minjie Shao, Tielin Shi, Qi Xia^*, Shiyuan Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2685-2703, 2025, DOI:10.32604/cmes.2025.068078 - 30 September 2025

Abstract A data-driven model of multiple variable cutting (M-VCUT) level set-based substructure is proposed for the topology optimization of lattice structures. The M-VCUT level set method is used to represent substructures, enriching their diversity of configuration while ensuring connectivity. To construct the data-driven model of substructure, a database is prepared by sampling the space of substructures spanned by several substructure prototypes. Then, for each substructure in this database, the stiffness matrix is condensed so that its degrees of freedom are reduced. Thereafter, the data-driven model of substructures is constructed through interpolation with compactly supported radial basis More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

Helen Shin Huey Wee, Nur Syazreen Ahmad^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025

Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

Long Huang^1,2,3,*, Junjia Zou³, Baoqing Liu¹, Zhijiang Jin^1,2, Jinyuan Qian¹

Frontiers in Heat and Mass Transfer, Vol.22, No.6, pp. 1613-1643, 2024, DOI:10.32604/fhmt.2024.058231 - 19 December 2024

Abstract This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat exchangers. The data were generated by experimentally validated Computational Fluid Dynamics (CFD) simulations of air-to-water microchannel heat exchangers. A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Gaussian Process Regression (GPR). These models are adeptly applied to predict air-side heat transfer performance with high precision, with ANN and GPR exhibiting notably superior accuracy. Additionally, this research further delves into… More >

Jianyong Ao¹, Yanping Li¹, Shengqing Hu¹, Songyu Gao², Qi Yao^2,*

Energy Engineering, Vol.121, No.12, pp. 3825-3841, 2024, DOI:10.32604/ee.2024.055250 - 22 November 2024

Abstract Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are… More >

Qin Qian¹, Mingjing Lu^1,2,*, Anhai Zhong¹, Feng Yang¹, Wenjun He¹, Min Li¹

Energy Engineering, Vol.121, No.8, pp. 2167-2190, 2024, DOI:10.32604/ee.2024.049430 - 19 July 2024

Abstract The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells… More >

Mehdi Jamei¹, Nadjem Bailek^2,*, Kada Bouchouicha³, Muhammed A. Hassan⁴, Ahmed Elbeltagi⁵, Alban Kuriqi⁶, Nadhir Al-Ansar⁷, Javier Almorox⁸, El-Sayed M. El-kenawy^9,10

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406 - 22 September 2022

Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF),… More >

Jiefeng Chen¹, Lisha Ding¹, Pengyu Wang¹, Weijin Zhang², Jie Li³, Badr A. Mohamed⁴, Jie Chen¹, Songqi Leng¹, Tonggui Liu¹, Lijian Leng^2,*, Wenguang Zhou^1,*

Journal of Renewable Materials, Vol.10, No.6, pp. 1555-1574, 2022, DOI:10.32604/jrm.2022.018625 - 20 January 2022

Abstract Biomass is a carbon-neutral renewable energy resource. Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution. For time- and cost-saving, it is vital to establish predictive models to predict biochar properties. However, limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar. Therefore, the multi-linear regression (MLR) and the machine learning (ML) models were developed to predict the measured HHV of biochar from the experiment data of this study. In detail, 52 types of biochars were produced by pyrolysis… More >

Seyoung Kim¹, Jeonghoon Choi¹, Kwang Ryel Ryu^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 207-220, 2022, DOI:10.32604/iasc.2022.021752 - 26 October 2021

Abstract Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation More >