An Adaptive Network Fuzzy Inference System Approach for Site investigation

Abstract

Site investigation has to be effective and must be carried out in a systematic way. The purpose of this article is to evaluate the number of investigation points, field tests, and laboratory tests for a description of a building site. Such an assessment depends on many parameters based on experiences which cannot be physically evaluated. The guidance on spacing is available from many sources, and such guidance provides a starting point for the extent of investigation. The recommendations were examined and used for building of the model to predict the optimal number of investigation points. Several parameters with the biggest influence on the number of investigation points were considered. The influence of each parameter was determined on the basis of recommendations and engineering judgment. Increments of the minimum number of investigation points for a different building site conditions were used to construct the model with adaptive network fuzzy inference system (ANFIS). The formed ANFIS-SI model was applied on reference cases. There is a good agreement between the model and the reference cases. Additionally, the recommendations for the type and frequency of tests in each stratum are provided to optimize the soil investigation. The ANFIS-SI model, with integrated recommendations, can be used as a systematic decision support tool for engineers to evaluate the number of investigation points for a description of the building site.

Foundry Wastes as a Potential Precursor in Alkali Activation Technology

“…The latter reference introduced a prediction of the minimal investment costs for the UGSs, with capacities from 10 to 100 million m 3 of natural gas, with the help of an adaptive network based fuzzy inference system (ANFIS). Analyses of the LRC/UGS with the ANFIS were reported by Žlender et al (2012, 2013) and Jelušič and Žlender (2014).…”

Discrete Optimization with Fuzzy Constraints

“…Additionally, the proposed ANFIS techniques include fuzzy clustering (FCM), which searches for patterns in data points [22]. The study also suggests that deflection limits could be reconsidered in the future by the experts who have a prolonged or intense experience through practice [23]. The advantages of using the exhaustive enumeration are reduced optimum weight values and obtaining a solution that is at a global optimum.…”

Predicting Geotechnical Investigation Using the Knowledge Based System

“…Using this system, the equivalent investigation ratio ( EQ ) is expressed as a function of input factors (see (13)). However, the main difference from paper [15] is that this paper provides additionally the optimal number of laboratory and field tests and not only the number of investigation points.…”

“…These factors include type of structure, project area, stratification, layout geometry, loading/sensitivity of structure, site geology, complexity of geological structure, variability of soil layers, and the similarity between preliminary and existing surrounding environment. For this paper, a system of ANFIS input factors is presented; an evaluation of these factors is shown in [15]. Using this system, the equivalent investigation ratio ( EQ ) is expressed as a function of input factors (see (13)).…”

“…Decision Criteria. The criteria (Table 3) which were presented in [15] were developed based on past experience Advances in Fuzzy Systems 7 LG = 2 PC = 4 GEO = 5 SIM = 5 Input values for the ANFIS-SI model (see Table 4) were determined based on the criteria shown in Table 3.…”