Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance
This study aims to accurately predict abrasion resistance, measured through the Los Angeles (LA) abrasion test, and modulus of elasticity, assessed using the Micro-Deval Abrasion (MDA) test, to support structural integrity and efficient material use in construction projects. We applied multi-output...
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MDPI AG
2024-12-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/1/37 |
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| author | Shaheen Mohammed Saleh Ahmed Hakan Güneyli Süleyman Karahan |
| author_facet | Shaheen Mohammed Saleh Ahmed Hakan Güneyli Süleyman Karahan |
| author_sort | Shaheen Mohammed Saleh Ahmed |
| collection | DOAJ |
| description | This study aims to accurately predict abrasion resistance, measured through the Los Angeles (LA) abrasion test, and modulus of elasticity, assessed using the Micro-Deval Abrasion (MDA) test, to support structural integrity and efficient material use in construction projects. We applied multi-output machine learning models—specifically Linear Regression (LR), Huber, RANSAC, and Support Vector Regression (SVR)—to predict LA and MDA values based on primary input parameters, including Uniaxial Compression Strength (UCS), Point Load Index (PLI), Schmidt Hammer Rebound (Sh_h), and Ultrasonic Pulse Velocity (UPV). The experimental work involved assessing model performance using metrics such as Mean Absolute Error (MAE), R-squared (R<sup>2</sup>), and Mean Squared Error (MSE). Linear Regression demonstrated superior predictive accuracy, achieving 94% for R<sup>2</sup> with an MAE of 0.21 and MSE of 0.09 for LA predictions and 92% for R<sup>2</sup> with an MAE of 0.24 and MSE of 0.11 for MDA predictions. These results underscore the potential of machine learning techniques in accurately predicting critical material properties, offering engineers reliable tools for optimizing material selection and structural design. This research contributes to the advancement of construction practices, promoting the development of durable and efficient infrastructure. |
| format | Article |
| id | doaj-art-169933459e9d4806bffc00cc32f6b69d |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-169933459e9d4806bffc00cc32f6b69d2025-01-10T13:15:51ZengMDPI AGBuildings2075-53092024-12-011513710.3390/buildings15010037Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion ResistanceShaheen Mohammed Saleh Ahmed0Hakan Güneyli1Süleyman Karahan2Geology Department, College of Science, Kirkuk University, Kirkuk 36001, IraqGeology Department, Faculty of Engineering, Cukurova University, Adana 01330, TurkeyGeology Department, Faculty of Engineering, Cukurova University, Adana 01330, TurkeyThis study aims to accurately predict abrasion resistance, measured through the Los Angeles (LA) abrasion test, and modulus of elasticity, assessed using the Micro-Deval Abrasion (MDA) test, to support structural integrity and efficient material use in construction projects. We applied multi-output machine learning models—specifically Linear Regression (LR), Huber, RANSAC, and Support Vector Regression (SVR)—to predict LA and MDA values based on primary input parameters, including Uniaxial Compression Strength (UCS), Point Load Index (PLI), Schmidt Hammer Rebound (Sh_h), and Ultrasonic Pulse Velocity (UPV). The experimental work involved assessing model performance using metrics such as Mean Absolute Error (MAE), R-squared (R<sup>2</sup>), and Mean Squared Error (MSE). Linear Regression demonstrated superior predictive accuracy, achieving 94% for R<sup>2</sup> with an MAE of 0.21 and MSE of 0.09 for LA predictions and 92% for R<sup>2</sup> with an MAE of 0.24 and MSE of 0.11 for MDA predictions. These results underscore the potential of machine learning techniques in accurately predicting critical material properties, offering engineers reliable tools for optimizing material selection and structural design. This research contributes to the advancement of construction practices, promoting the development of durable and efficient infrastructure.https://www.mdpi.com/2075-5309/15/1/37multi-output regressionLAMDAabrasionmachine learning |
| spellingShingle | Shaheen Mohammed Saleh Ahmed Hakan Güneyli Süleyman Karahan Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance Buildings multi-output regression LA MDA abrasion machine learning |
| title | Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance |
| title_full | Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance |
| title_fullStr | Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance |
| title_full_unstemmed | Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance |
| title_short | Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance |
| title_sort | predictive analytics in construction multi output machine learning models for abrasion resistance |
| topic | multi-output regression LA MDA abrasion machine learning |
| url | https://www.mdpi.com/2075-5309/15/1/37 |
| work_keys_str_mv | AT shaheenmohammedsalehahmed predictiveanalyticsinconstructionmultioutputmachinelearningmodelsforabrasionresistance AT hakanguneyli predictiveanalyticsinconstructionmultioutputmachinelearningmodelsforabrasionresistance AT suleymankarahan predictiveanalyticsinconstructionmultioutputmachinelearningmodelsforabrasionresistance |