Data-Driven Concrete Quality Optimization in Industry 4.0: Predictive Compressive Strength Modeling Through Machine Learning and Ensemble Approaches

As numerous manufacturing enterprises are progressing towards Industry 4.0, advanced predictive models are required to optimize contemporary construction practices. The precise prediction of the compressive strength of cement is crucial, as it is an integral material for any constructional unit. This research paper explores numerous advanced machine learning and ensemble learning techniques for effective concrete strength prediction, enabling proactive quality control measures in an Industry 4.0 based environment. The research utilizes an open-source dataset and employs advanced machine learning techniques to interpret and learn intricate relationships among input features, such as cement quantity, blast furnace slag content, fly ash ratios, water weight, superplasticizer usage, and coarse and fine aggregate proportions, as well as curing age for predictive modeling. Experimental results validate the Histogram-Based Gradient Boosting model as an optimal technique for effectivey forecasting the compressive strength of cement in Newtons per square millimeter (MPa), with a cross-validation R2 Score of 0.922. The findings of this research work contributes to the increasing demand for accurate and scalable predictive models within the quality control unit of an Industry 4.0 based manufacturing firm.