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Concrete is the most utilized material (i.e., average production of 2 billion tons per year) for the construction of buildings, bridges, roads, dams, and several other important infrastructures. The strength and durability of these structures largely depend on the compressive strength of the concrete. The compressive strength of concrete depends on the proportionality of the key constituents (i.e., fine aggregate, coarse aggregate, cement, and water). However, the optimization of the constituent proportions (i.e., matrix design) to achieve high-strength concrete is a challenging task. Furthermore, it is essential to reduce the carbon footprint of the cementitious composites through the optimization of the matrix. In this research, machine learning algorithms including regression models, tree regression models, support vector regression (SVR), ensemble regression (ER), and gaussian process regression (GPR) were utilized to predict the compressive and tensile concrete strength. Also, the model performance was characterized based on the number of input variables utilized. The dataset used in this research was compiled from journal publications. The results showed that the exponential GPR had the highest performance and accuracy. The model had an impressive performance during the training phase, with a R2 of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. In the testing phase, the model maintained its accuracy with a R2 of 0.99, RMSE of 0.0025134 MPa, and MAE of 0.0016367 MPa. In the training and testing phases, the exponential GPR also demonstrated high accuracy in predicting the tensile strength with an R2, RMSE, and MAE of 0.99, 0.00049247 MPa, and 0.00036929 MPa, respectively. Furthermore, in the prediction of tensile strength the number of variables utilized had an insignificant effect on the performance of the models. However, in predicting the compressive strength, an increase in the number of input variables lead to an enhancement in the performance metrics. The results of this research can allow for the quick and accurate prediction of the strength of a given concrete mixture design.

Abstract Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it’s the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs. [ABSTRACT FROM AUTHOR]