Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine learning techniques for estimation of Los Angeles abrasion value of rock aggregates
Rock aggregates are extensively used in the production of materials such as asphalt concrete and Portland cement concrete. Los Angeles Abrasion (LAA) value is one the basic characteristics of crushed aggregates that reflects their resistance against mechanical abrasive factors such as repeated impact loading. There have been several efforts to estimate the LAA value from surrogate physical and/or mechanical properties of the material. Previous works have mainly focussed on a limited number of data samples and thus may not be generalised to make predictions for different lithologies. Another drawback of the current approaches is that they are often in the form of one-to-one correlations between the LAA and a measure of mechanical behaviour such as the uniaxial strength. This paper investigates the capability of Machine Learning (ML) models for prediction of LAA value. Different material properties have been tested as the input parameters to achieve the best prediction results. It was observed that the ML models perform considerably better for predicting LAA compared to the existing correlations reported in the literature.
Machine learning techniques for estimation of Los Angeles abrasion value of rock aggregates
Rock aggregates are extensively used in the production of materials such as asphalt concrete and Portland cement concrete. Los Angeles Abrasion (LAA) value is one the basic characteristics of crushed aggregates that reflects their resistance against mechanical abrasive factors such as repeated impact loading. There have been several efforts to estimate the LAA value from surrogate physical and/or mechanical properties of the material. Previous works have mainly focussed on a limited number of data samples and thus may not be generalised to make predictions for different lithologies. Another drawback of the current approaches is that they are often in the form of one-to-one correlations between the LAA and a measure of mechanical behaviour such as the uniaxial strength. This paper investigates the capability of Machine Learning (ML) models for prediction of LAA value. Different material properties have been tested as the input parameters to achieve the best prediction results. It was observed that the ML models perform considerably better for predicting LAA compared to the existing correlations reported in the literature.
Machine learning techniques for estimation of Los Angeles abrasion value of rock aggregates
Asadi, Mojtaba (Autor:in) / TaghaviGhalesari, Abbasali (Autor:in) / Kumar, Saurav (Autor:in)
European Journal of Environmental and Civil Engineering ; 26 ; 964-977
17.02.2022
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Predicting the Los Angeles abrasion loss of rock aggregates from the uniaxial compressive strength
| British Library Online Contents | 2007
Relation between Los Angeles abrasion test results and service records of coarse aggregates
| Engineering Index Backfile | 1938
Relation between Los Angeles abrasion test results and service records of coarse aggregates
| Engineering Index Backfile | 1938