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Keywords = Marshall Mix Design Method (M2DM)

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20 pages, 5180 KiB  
Article
Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming
by Hamad Hassan Awan, Arshad Hussain, Muhammad Faisal Javed, Yanjun Qiu, Raid Alrowais, Abdeliazim Mustafa Mohamed, Dina Fathi and Abdullah Mossa Alzahrani
Buildings 2022, 12(3), 314; https://doi.org/10.3390/buildings12030314 - 7 Mar 2022
Cited by 42 | Viewed by 6098
Abstract
The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical [...] Read more.
The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC. Full article
(This article belongs to the Special Issue Advanced Sustainable Materials in Buildings)
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