Ali Dehghanbanadaki

The challenge of predicting relative dry density ( D _r ) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. An experimental dataset obtained from a mobile pluviator was utilised to develop and analyse various ML models. The mathematical analysis was centred on the optimisation and comparative performance of ensemble methods, with particular emphasis given to gradient boosting regression (GBR), AdaBoost regression, and extreme gradient boosting (XGBoost). The mathematical formulation of the GBR model was rigorously examined and optimised using advanced tuning functions, achieving exceptional performance metrics (mean squared error [MSE] = 11.91, mean absolute error [MAE] = 1.93, R ² = 0.997). Through sensitivity analysis, it was revealed that the distance between the shutter plate and the top sieve is the most significant factor affecting D _r prediction. A computational platform was developed within the Google Colab environment, demonstrating the practical application of the mathematical models. This research contributes to applied mathematics by showcasing advanced algorithmic approaches to solving complex geotechnical engineering problems while providing a rigorous mathematical foundation for future developments.

Traditional concrete structures are frequently linked to poor energy efficiency and substantial heat loss, which pose significant environmental issues. To enhance thermal insulation and reduce heat loss, the use of precast insulated walls is suggested. This research introduces a new energy-efficient precast concrete panel (PCP). We explored various material combinations, including air bubbles, nano microsilica compound (NMC), nano microsilica powder (NMP), and latex, to determine the most effective formulation. A total of 99 tests were performed to assess the compressive strength of the samples, with 28 tests selected for thermal conductivity evaluations at temperatures of 300 °C and 400 °C based on satisfactory compressive strength results. The results indicated that the optimal mix of 4% air bubbles and 13% NMC achieved the lowest thermal conductivities of 1.31 W/m·K and 1.20 W/m·K at 300 °C and 400 °C, respectively, showing improvement ratios of 7% and 15.5% compared to the baseline tests. Additionally, the tests that included latex did not meet the thermal conductivity standards. The optimal combinations identified in this research can be effectively utilized in PCPs, resulting in significant energy savings. It is expected that stakeholders in the green building sector will recognize these proposed PCPs as a practical energy-efficient solution to advance sustainable and environmentally friendly construction practices.

The use of deep cement mixing (DCM) columns is an effective and affordable technique for ground stabilization. However, designing this method can be complex due to uncertainties in the geotechnical properties of the soil and DCM columns, area improvement ratio, column arrangement, and required cement content. This study aimed to address this issue by using Gaussian process regression (GPR) models to estimate the ultimate bearing capacity (UBC) of soft soil improved with DCM columns.To create and train the GPR models, the study utilized a database of 46 physical modeling tests under end-bearing and floating conditions. The researchers used different kernel functions, including rational quadratic, squared exponential, Matern 5/2, and exponential, for the GPR models. The models were then optimized through Bayesian optimization and compared to other predictive techniques such as multilayer perceptron (MLP), radial basis function (RBF), and neuro-fuzzy inference systems (ANFIS) using test data.As a case study, the researchers evaluated a decision-making model for designing the geotechnical properties of DCM columns. The results showed that the optimized GPR model's accuracy in terms of performance indices was satisfactory for both end-bearing and floating DCM column conditions. The optimized GPR model outperformed MLP, RBF, and ANFIS performance indices using test data. Overall, the study demonstrated that optimized GPR models are a promising method for early prediction of stabilized ground UBC.