Fuzzy Knowledge Based System for Suitability of Soils in Airfield Applications

Proper design of roads and airfield pavements requires an in-depth soil properties evaluation to determine suitability of soil. Soft computing is used to model soil classification system's dynamic behaviour and its properties. Soft computing is based on methods of machine learning, fuzzy logic and artificial neural networks, expert systems, genetic algorithms. Fuzzy system is a strong method for mimicking human thought and solves question of confusion. This paper proposes a new decision-making approach for soil suitability in airfield applications without a need to perform any manual works like use of tables or chart. A fuzzy knowledge based approach is built to rate soil suitability in qualitative terms for airfield application. The proposed model describes a new technique by defining fuzzy descriptors using triangular functions considering the index properties of soils as input parameters and fuzzy rules are generated using fuzzy operators to classify soil and rate its suitability for airfield applications. The data obtained from the results of the laboratory test are validated with the results of the fuzzy knowledge-based system indicating the applicability of the Fuzzy model created. The approach developed in this work is more skilled to other prevailing optimization models. Due to its system's flexibility, it can be suitably customized and applied to laboratory test data available, thus delivering a wide range for any geotechnical engineer.

Automated summary

This paper introduces a new decision-making approach for assessing soil suitability in airfield applications using soft computing method, utilizing fuzzy knowledge to qualitatively evaluate the suitability of soil.