A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 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 (R2), and Mean Squared Error (MSE). Linear Regression demonstrated superior predictive accuracy, achieving 94% for R2 with an MAE of 0.21 and MSE of 0.09 for LA predictions and 92% for R2 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.
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 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 (R2), and Mean Squared Error (MSE). Linear Regression demonstrated superior predictive accuracy, achieving 94% for R2 with an MAE of 0.21 and MSE of 0.09 for LA predictions and 92% for R2 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.
Predictive Analytics in Construction: Multi-Output Machine Learning Models for Abrasion Resistance
Shaheen Mohammed Saleh Ahmed (author) / Hakan Güneyli (author) / Süleyman Karahan (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
MACHINE LEARNING 101: IS PREDICTIVE ANALYTICS POSSIBLE IN YOUR FACILITY?
British Library Online Contents | 2018
BUCKET FOR CONSTRUCTION EQUIPMENT WITH ENHANCED ABRASION RESISTANCE AND IMPACT RESISTANCE
European Patent Office | 2017
Kidney Failure Detection and Predictive Analytics for ckd Using Machine Learning Procedures
| Online Contents | 2022