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The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength (CS) of environmentally friendly (EF) concrete containing recycled aggregate is important for understanding sustainable structures’ concrete behaviour. In this research, the capability of the deep learning neural network (DLNN) approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence (AI) approaches named multivariate adaptive regression spline (MARS), extreme learning machines (ELMs), and random forests (RFs). The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting (XGBoost) was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error (RMSE = 2.23). The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete.

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling.