Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Abstract

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

Low-Cost Fiber Chopped Strand Mat Composites for Compressive Stress and Strain Enhancement of Concrete Made with Brick Waste Aggregates

“…Therefore, there exists a natural urge to reuse this waste in construction applications to avoid issues related to their disposal. A general consensus is that optimal advantages are associated with the use of recycled aggregates as compared to the use of natural aggregates in concrete [ 11 , 12 , 13 , 14 ]. Ohemeng et al [ 2 ] concluded that the production of 1 ton of recycled aggregate concrete was 40% cheaper than the cost of natural aggregate concrete having the same volume.…”

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

“…Bridging the gaps from atomistic through macroscale behavior of concrete can enable innovation at all necessary scales to advance the development of sustainable concrete. 248 Finally, several machine learning methods have also been used to assess and optimize concrete strength, 249−253 including "better-choice" concrete hybrids that incorporate natural reinforcements or other industrial waste products such as rice husk, 254 sugar cane bagasse ash, 255 green fly ash, 256 zeolite, 257 and silica fume. 258 For more information, we refer the reader to review papers focused on concrete durability.…”

“…Finally, several machine learning methods have also been used to assess and optimize concrete strength, − including “better-choice” concrete hybrids that incorporate natural reinforcements or other industrial waste products such as rice husk, sugar cane bagasse ash, green fly ash, zeolite, and silica fume . For more information, we refer the reader to review papers focused on concrete durability …”

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