Data-driven models to predict the water-to-cement ratio and initial setting time of cement grouts

As a common defect, the water–cement ratio of grout can currently be monitored only during the maintenance phase, which limits the repair methods and misses the optimal opportunity for repairs. To overcome this limitation, this study integrated ultrasonic parameters previously used to characterise cement-based materials and developed a new Initial Setting Time and Water–Cement Ratio (IST_WCR) risk model to predict the setting time and water–cement ratio grout using machine learning (ML) algorithms. Experiments on grout involved four different water–cement ratios, ranging from 0.11 to 0.18. A data-driven method based on ML was used to extract predictive factors from eight ultrasonic parameters, including the speed, energy, main frequency, and main frequency amplitude of P-waves and S-waves, and to evaluate multiple ML classifiers to establish the IST_WCR risk prediction model. This model underwent internal and external cross-validations and demonstrated very strong performance with a Brier score of under 0.01. The dataset for ML classifiers contained a total of 956 signals and 7648 features. Compared with traditional methods, this method can automatically characterise the setting process of grout and identify defective water–cement ratios at a very early stage of curing.