Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review

Journal of Building Engineering, Год журнала: 2023, Номер 80, С. 108065 - 108065

Опубликована: Ноя. 3, 2023

Язык: Английский

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Utilizing machine learning approaches within concrete technology offers an intelligent perspective towards sustainability in the construction industry: a comprehensive review

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Окт. 26, 2024

Язык: Английский

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Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Computer Modeling in Engineering & Sciences, Год журнала: 2024, Номер 140(2), С. 1595 - 1617

Опубликована: Янв. 1, 2024

Blasting in surface mines aims to fragment rock masses a proper size.However, flyrock is an undesirable effect of blasting that can result human injuries.In this study, support vector regression (SVR) combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony (ACO), and whale (WOA) for predicting two Iran.Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), general (GRNN), are employed, their performances compared those hybrid SVR models.After modeling, the measured predicted values validated some performance indices, such as root mean squared error (RMSE).The results revealed SVR-WOA model has most optimal accuracy, RMSE 7.218, while RMSEs KELM, GRNN, SVR-GSA, ANN, SVR-BBO, SVR-ACO models 10.668, 10.867, 15.305, 15.661, 16.239, 18.228, respectively.Therefore, combining WOA be valuable tool accurately distance mines.

Язык: Английский

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Machine Learning-Driven Design of Rare Metal Doped Niobium Alloys with Enhanced Strength and Ductility

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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A review of crack research in concrete structures based on data-driven and intelligent algorithms

Structures, Год журнала: 2025, Номер 75, С. 108800 - 108800

Опубликована: Апрель 8, 2025

Язык: Английский

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Bond strength and failure mode prediction model for recycled aggregate concrete based on intelligent algorithm optimized support vector machine

Structures, Год журнала: 2024, Номер 71, С. 107999 - 107999

Опубликована: Дек. 20, 2024

Язык: Английский

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A novel local deformation pipe section identification method via IMU detection data and hybrid deep learning model

Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 224, С. 112091 - 112091

Опубликована: Окт. 31, 2024

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A deep extreme learning machine approach optimized by sparrow search algorithm for forecasting of traffic flow

Physica Scripta, Год журнала: 2024, Номер 99(12), С. 125288 - 125288

Опубликована: Ноя. 14, 2024

Abstract Traffic flow modeling has a pivotal role within Intelligent Transportation Systems (ITSs), holding vital importance in alleviating traffic congestion and decreasing carbon emissions. Due to the presence of variability nonlinear attributes flow, developing an effective resilient model for predicting poses significant challenge. Precisely is not merely feasible issue; it also difficulties researchers involved this field. This study proposes hybrid predictive forecast flow. The proposed effectively merges strengths Sparrow Search algorithm (SSA) Multi-layer Extreme Learning Machine (ML-ELM) model, enhancing prediction accuracy. SSA optimization technique applied optimize initial weights bias parameters ML-ELM model. ELM approach machine learning that employs single hidden layer address various tasks. However, situations where more complex problems are encountered, extends concept by incorporating multiple layers enhance its capabilities challenges effectively. Finally, utilized achieve optimal tuning hyperparameters context improve Compared other selected models, outperforms them terms performance metrics, including Root Mean Square Errors (RMSE), Absolute (MAE), Percentage (MAPE) Correlation Coefficients (r), indicating appropriate task.

Язык: Английский

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Uncertainty quantification and global sensitivity analysis of bearing capacity of push-out specimen considering randomness in bond-slip behaviour

Journal of Building Engineering, Год журнала: 2024, Номер 91, С. 109591 - 109591

Опубликована: Май 10, 2024

Язык: Английский

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Research on a Method for Classifying Bolt Corrosion Based on an Acoustic Emission Sensor System

Sensors, Год журнала: 2024, Номер 24(15), С. 5047 - 5047

Опубликована: Авг. 4, 2024

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is paramount importance for common fault repair accident prevention. This paper aims to detect classify corrosion levels accurately. We design implement classification system based on Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm applied identify optimal combination. Subsequently, Extreme Learning Machine (ELM) model utilized classification. Additionally, achieve high prediction accuracy, an improved goose (GOOSE) employed ensure most suitable parameter combination ELM model. Experimental measurements were conducted five classes levels: 0%, 25%, 50%, 75%, 100%. The accuracy obtained using proposed method was at least 98.04%. Compared state-of-the-art diagnostic models, our approach exhibits superior AE signal recognition performance stronger generalization ability adapt variations working conditions.

Язык: Английский

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