The Application of Artificial Intelligence Methods on the Optimizing Improvement Depth of Dynamic Compaction

Abstract

Dynamic Compaction (DC) with tamping is one of the soil improvement methods and has been significantly accepted due to its advantages over other soil improvement methods. In this paper, an attempt has been made to use the fuzzy logic and Sugeno inference system to investigate the effect of the parameters involved in soil improvement operations using the DC method on the relative improvement depth. A correlation has been proposed to estimate the effective depth for the granular soils and compared to the fuzzy results. The Particle Swarm Optimization (PSO) algorithm has been used to achieve maximum improvement depth. The results indicate that the interaction between tamper weight and height of tamping plays the most important role in the design procedures. The results reveal that by using PSO, the maximum depth of improvement is increased by 33%. Studies show that the optimal tamper radius for most of the compaction patterns with medium to high applied energies is equal to 1.5 to 2 meters, the optimal numbers of drops are equal to 25, and the optimal grid spacing is equal to 6 to 7 meters. It should be noted that using PSO dynamic compaction pattern; the maximum improvement depth has been achieved.

Competency Assessment Based on Fuzzy Logic and Artificial Intelligence Mechanism: A Study of Competency Assessment Document for the Algerian SEROR Company

“…Therefore, organisations need to search for an effective, accurate, and inexpensive system that ensures proper evaluation, reduces the impact of the psychological aspect of the evaluator when carrying out the evaluation process, and helps to evaluate the largest number of workers in a short time and at the lowest costs. For that, we relied in our analysis for the competency assessment document of the SEROR Company, which holds an ISO certificate, on quantitative data collection tools through the use of Fuzzy Logic, which is a useful framework for dealing with uncertainty in logic programming (Angelov, 2004), a data processing base (FisPro) which allows the creation of fuzzy inference systems (Koohsari et al, 2023), and an Artificial Intelligence Mechanism, with a discussion before presenting the general conclusion of our research.…”