Prediction of the cementing potential of activated pond ash reinforced with glass powder for soft soil strengthening, by an artificial neural network model

¹ Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
² Department of Civil Engineering, School of Engineering, University of the Peloponnese, GR-26334 Patras, Greece
³ School of Science and Technology, Hellenic Open University, GR-26335 Patras, Greece
⁴ Department of Structural Engineering, Future University in Egypt, New Cairo, Egypt
⁵ Department of Civil Engineering, Ladoke Akintola University of Technology, Ogbomosho, Nigeria

^* Corresponding author: kontoni@uop.gr

Abstract

The effect of Pond Ash (PA) activated with sodium chloride (NaCl) solution and reinforced with glass powder on the mechanical properties of soft clay soil, which comprise of the California bearing ratio (CBR) and the unconfined compressive strength (UCS) has been studied in this research work. The PA requires pozzolanic improvements to meet the ASTM C618 requirements for pozzolanas. In the present research paper, further emphasis has been on the machine learning prediction of CBR and UCS of the soft clay soil stabilized with a composite of PA. Generally, the studied soft clay soil properties, which were the microstructure, microspecter/micrograph, oxide composition, Atterberg limits, compaction behavior, free swell index (FSI), CBR and UCS significantly improved due to the enhanced cementitious ability of the activated and reinforced PA. The multiple data collected from this general stabilization result were used to predict the soil’s CBR and UCS by the artificial neural network (ANN) technique. The results showed high performance of the model in terms of the sum of squares error (SSE) of 1.5% and 2.0% and the coefficient of determination (R²) of 0.9979 and 0.9973 for the CBR and UCS models, respectively. The models also outclassed the performances of other models from the literature.