... aims to demonstrate the applicability of machine learning–based forecasting for reliable micro-hydropower feasibility assessments. Design/methodology/approach Daily flow rate data obtained from a pressurized drinking water transmission pipeline were analyzed as time-series. Flow-duration curves...

... simulations. Design/methodology/approach A computational framework integrating phase-field modeling (PFM) and machine learning (ML) is developed. A strain-orthogonal phase-field formulation is employed to simulate interfacial damage and crack propagation in SFRC across different concrete grades and fiber...

...Mirza Pasic; Aleksandar Zivkovic; Kenan Muhamedagic; Dejan Marinkovic; Derzija Begic-Hajdarevic Purpose The purpose of this paper is to develop machine learning (ML) models for prediction of surface roughness and cutting forces of 42CrMo4 steel in hard turning process. Design/methodology...

..., a complete workflow for machine learning (ML)-based hardness prediction of Cu thin films is proposed. This work effectively captures the nonlinear relationships between the physical properties such as elastic modulus, externally applied pressure, displacement and grain size of Cu thin films. Design...

... learning machine Machine learning Starfish optimization algorithm The Extreme Learning Machine (ELM) is defined as a neural network architecture consisting of an input layer, a single hidden layer, and an output layer (Huang et al., 2006). This model is notable for its relatively...

... measures, which were then used as temporal features in developing a match-winner prediction model. Findings A machine learning model trained on the extracted temporal features achieved 79% accuracy in predicting match winners and demonstrated a negative correlation between match-winning performances...

...Reda Yahiaoui; Salima Senhadji Purpose This study addresses the high computational cost and complexity of conventional numerical methods for predicting the geometric correction factor (β-factor) associated with the stress intensity factor. It introduces a machine learning (ML) framework...

... Licensed re-use rights only Chemical industry has traditionally relied on mathematical modeling to understand chemical processes, generating vast data sets. AI, especially machine learning, now helps manage and detect patterns in this data, facilitating larger systems without substantial resources...

...Deeksha Tripathi; Saroj K. Biswas Purpose Accurate crop yield prediction (CYP) is essential for enhancing agricultural productivity, ensuring food security and enabling sustainable resource management. Machine learning (ML) algorithms have become popular in CYP because they can estimate crop...

... label y i as it’s the original point x i ensuring label preservation: Data augmentation Machine learning Manufacturing Deep drawing The digitalisation is having a profound impact on both the societal and the economic landscape, presenting both new possibilities...

... Decision tree Finite element simulation Machine learning Random forest Support vector machine Vacuum assisted resin infusion Wind turbine blade manufacturing Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit...

... analysis and foster broader adoption of AI in aeronautics. Therefore, the integration of machine learning techniques into aerodynamic analysis represents a transformative shift in the field of aeronautics. ML empowers researchers and engineers with tools to optimize aircraft shapes, predict aerodynamic...

..., offering practical guidance for implementing AI-driven solutions to enhance operational reliability and efficiency in industries reliant on complex, dynamic machinery. Design/methodology/approach This study employs a comparative analysis of three machine learning algorithms – neural networks, random...

...(s): Authors’ own work A robust and reliable method in machine learning for evaluating a predictive model’s performance is K-fold cross-validation (CV). Using this approach, a database is divided into ’K’ subgroups of roughly equal size. After that, the model is trained and assessed ’K...

...Klaus Jürgen Folz; Herbert Martins Gomes Purpose The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs), when classifying seven states of operation of an electric motor using...

..., translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode Machine learning Inverse...

... behavior. Design/methodology/approach This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically...

.... The objective of project completion time can be calculated as per to Eqns. (1–2) . Multi-objective optimization Aquila optimizer Opposition-based learning Pareto-front solutions Machine learning Quantitatively measuring project quality (PQ) poses a significant challenge in construction...

... alternative methods of identifying material parameters. Jiří Halamka can be contacted at: jiri.halamka@fs.cvut.cz 29 02 2024 21 06 2024 23 06 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Machine learning Artificial neural...

... spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships. Findings This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial...