Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study

This study evaluated the compressive strength (CS) of cement-based materials (CBMs) incorpo-rating eggshell powder (ESP) as a partial substitute for cement and fine aggregate using both experimental and machine learning (ML)-based strategies. Initially, the CS of CBMs based on ESP was measured experimentally. ML techniques, including support vector machine and bagging regressor, were applied for CS estimation. Comparing the coefficient of determination (R2), sta-tistical checks, k-fold evaluation, and measuring the difference between experimental and pro-jected CS were used to evaluate the performance of ML models. According to the results of the experiment, the addition of ESP increased the CS of CBMs when used in lower replacement ratios. In addition, the support vector machine model had a reasonable degree of precision, while the bagging regressor model predicted the CS of ESP-based CBMs with a higher degree of precision. Incorporating waste eggshells into building materials will promote sustainable development by minimizing environmental problems connected with eggshell disposal, conserving natural re-sources, offering cost-effective materials, and decreasing CO2 emissions. In addition, the use of ML approaches will benefit the construction sector by facilitating efficient and economical ways of analyzing the properties of materials.

Automated summary

This study evaluated the compressive strength of cement-based materials incorporating eggshell powder as a partial substitute using both experimental and machine learning strategies. The experiment showed that adding eggshell powder increased the compressive strength of the materials at lower replacement ratios. Machine learning models, especially the bagging regressor, predicted the compressive strength with a higher degree of precision. The use of waste eggshells in building materials has environmental and cost benefits, while machine learning approaches benefit the construction sector by analyzing material properties efficiently and economically.