Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review

Results in Engineering, Год журнала: 2024, Номер 22, С. 102305 - 102305

Опубликована: Май 22, 2024

Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality environmental risks provided by pollutant data crucial for management. The use artificial neural network (ANN) approaches predicting pollutants reviewed this research. These methods are based on several forecast intervals, including hourly, daily, monthly ones. This study shows that ANN techniques contaminants more precisely than traditional methods. It has been discovered input parameters architecture-type algorithms used affect accuracy prediction models. therefore accurate reliable other empirical models because they can handle wide range meteorological parameters. Finally, research gap networks identified. review may inspire researchers to certain extent promote development intelligence prediction.

Язык: Английский

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Predictive models in machine learning for strength and life cycle assessment of concrete structures

Automation in Construction, Год журнала: 2024, Номер 162, С. 105412 - 105412

Опубликована: Апрель 3, 2024

Язык: Английский

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Polypropylene waste plastic fiber morphology as an influencing factor on the performance and durability of concrete: Experimental investigation, soft-computing modeling, and economic analysis

Construction and Building Materials, Год журнала: 2024, Номер 438, С. 137244 - 137244

Опубликована: Июль 2, 2024

Язык: Английский

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A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete

Construction and Building Materials, Год журнала: 2024, Номер 440, С. 137370 - 137370

Опубликована: Июль 16, 2024

Язык: Английский

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Год журнала: 2025, Номер 71, С. 108138 - 108138

Опубликована: Янв. 1, 2025

Язык: Английский

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Forecasting interfacial bond strength in FRP-reinforced concrete using soft computing techniques

Construction and Building Materials, Год журнала: 2025, Номер 473, С. 140827 - 140827

Опубликована: Март 30, 2025

Язык: Английский

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Interpretable machine learning models for predicting the bond strength between UHPC and normal-strength concrete

Materials Today Communications, Год журнала: 2024, Номер 40, С. 110006 - 110006

Опубликована: Июль 30, 2024

Язык: Английский

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Application of optimized random forest simulation on flexural strength of basalt fiber reinforced concrete

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(4)

Опубликована: Фев. 26, 2025

Язык: Английский

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Machine learning-enabled characterization of concrete mechanical strength through correlation of flexural and torsional resonance frequencies

Physica Scripta, Год журнала: 2024, Номер 99(7), С. 076002 - 076002

Опубликована: Май 21, 2024

Abstract In this study, an assessment of concrete compressive strength was conducted using impulse excitation data-driven machine learning (ML) framework. The model constructed upon a deep neural network and aided by the backpropagation method, ensuring precise training process. contrast to prior research, which mainly focused on mixture components, meaningful relationship between physical parameters—resonant frequencies elastic moduli—and established our ML model. Remarkable performance demonstrated, with root mean square error value 2.8MPa determination factor 0.97. Through Pearson analysis, correlations input features output targets, ranging from −0.29 0.90, were revealed. Notably, strongest found in Young's shear moduli, derived flexural torsional frequencies, highlighting pivotal role dynamic response concrete's mechanical behavior. Furthermore, findings indicated slight prediction deviations cases involving samples high Poisson's ratio. This work illuminates potential for accurate leveraging response, particularly modes, thereby opening avenues research into without direct consideration sample ingredients.

Язык: Английский

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Comparative analysis of conventional and ensemble machine learning models for predicting split tensile strength in thermal stressed SCM-blended lightweight concrete

Materials Today Proceedings, Год журнала: 2024, Номер unknown

Опубликована: Апрель 1, 2024

Язык: Английский

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