Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

Jan, Muhammad; Hussain, Sajjad; Zahra, Rabab; Emad, Muhammad Zaka; Khan, Naseer Muhammad; Rehman, Zahid Ur; Cao, Kewang; Alarifi, Saad S.; Raza, Salim; Sherin, Saira

doi:10.3390/su15118835

Cited by 9 publications

(1 citation statement)

References 73 publications

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“…Let, D = (x i , y i ) be a sample dataset including the number of x i input features and y i target feature. The objective function of XgBoost is a sum of the loss function and regularization term and is defined as [41]:…”

Section: ) Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

Rafiq,

Mahmood,

Ahmed

et al. 2024

IEEE Access

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The occupancy datasets are useful for planning important buildings' related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants' count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants' count in each room. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively.

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Section: ) Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

Rafiq,

Mahmood,

Ahmed

et al. 2024

IEEE Access

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Liu,

Su,

Sun

et al. 2024

Artif Intell Rev

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Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

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AI-powered simulation models for estimating the consolidation settlement of shallow foundations

Jagan,

Samui

2024

Model. Earth Syst. Environ.

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Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

Cited by 9 publications

References 73 publications

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Types

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

AI-powered simulation models for estimating the consolidation settlement of shallow foundations

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