Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson's ratio of selected rock types.

This research focuses on the predictive modeling between rocks' dynamic properties and the optimization of neural network models. For this purpose, the rocks' dynamic properties were measured in terms of quality factor (Q), resonance frequency (FR), acoustic impedance (Z), oscillation decay factor (α), and dynamic Poisson's ratio (v). Rock samples were tested in both longitudinal and torsion modes. Their ratios were taken to reduce data variability and make them dimensionless for analysis. Results showed that with the increase in excitation frequencies, the stiffness of the rocks got increased because of the plastic deformation of pre-existing cracks and then started to decrease due to the development of new microcracks. After the evaluation of the rocks' dynamic behavior, the v was estimated by the prediction modeling. Overall, 15 models were developed by using the backpropagation neural network algorithms including feed-forward, cascade-forward, and Elman. Among all models, the feed-forward model with 40 neurons was considered as best one due to its comparatively good performance in the learning and validation phases. The value of the coefficient of determination (R2 = 0.797) for the feed-forward model was found higher than the rest of the models. To further improve its quality, the model was optimized using the meta-heuristic algorithm (i.e. particle swarm optimizer). The optimizer ameliorated its R2 values from 0.797 to 0.954. The outcomes of this study exhibit the effective utilization of a meta-heuristic algorithm to improve model quality that can be used as a reference to solve several problems regarding data modeling, pattern recognition, data classification, etc.

A Novel Application of the Hydrophobic Polyurethane Foam: Expansive Soil Stabilization.

The reversible shrink-swell behavior of expansive soil imposes a serious challenge that threatens the overlying structures' safety and durability. Traditional chemical additives such as lime and cement still exhibit satisfying performance over their counterparts in terms of swelling potential reduction. Nevertheless, significant concerns are associated with these chemicals, in addition to their environmental impact. This paper proposes a novel application of the closed-cell one-component hydrophobic polyurethane foam (HPUF) to stabilize the swelling soil. An extensive experimental study was performed to assess the efficiency of HPUF in mitigating both the swelling and shrinkage response of high montmorillonite content expansive soil. Expansive soil was injected/mixed with different weight ratios of the proposed stabilizer, and the optimum mixing design and injection percentage of the foam resin were identified to be ranged from 10% to 15%. The shrink-swell behaviors of both injected and noninjected samples were compared. Results of this comparison confirmed that HPUF could competently reduce both the swelling potential and the shrinkage cracking of the reactive expansive soil, even after several wet-shrink cycles.