Shrinkage Limit Multi-AI-Based Predictive Models for Sustainable Utilization of Activated Rice Husk Ash for Treating Expansive Pavement Subgrade

Autor: Ifeyinwa Ijeoma Obianyo, Kennedy C. Onyelowe, Light I. Nwobia, Ahmed M. Ebid
Rok vydání: 2021
Předmět:
Zdroj: Transportation Infrastructure Geotechnology. 9:835-853
ISSN: 2196-7210
2196-7202
DOI: 10.1007/s40515-021-00199-y
Popis: Swell-shrink phenomenon experienced by foundation soils like the subgrade layer is a major cause of failures in pavement facilities. This phenomenon is fundamentally caused by rise in water table and moisture ingress from runoff, and through pavement surface cracks and lateral movement of water from location to location. Repeated laboratory trails prior to design and construction can actually be avoided by using soft computing–based predictive models to propose model expressions, which can be used during the design stage and subsequently to monitor the behavior and performance of the structure. This research utilized the intelligent abilities of genetic programming (GP), artificial neural network (ANN) and genetic algorithm (GA), and optimized polynomial linear regression (PLR) known as the evolutionary polynomial regression (EPR) to forecast the shrinkage limit of expansive soil treated with rice husk ash (RHA) and different quicklime dosage–activated rice husk ash. At the end of prediction, performance indices, i.e., R2 and SSE, were used to test the accuracy of the models. It was observed that EPR outclassed ANN and GP with indices of 0.974 and 1.4%, respectively. Meanwhile, the composites of RHA showed significant improvement on the shrinkage limit of the treated soil for use as a compacted subgrade material.
Databáze: OpenAIRE
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