Bojan Žlender

Planning Geotechnical Investigation Using ANFIS

Engineering experience may be written in mathematical form by using adaptive network-based fuzzy inference system (ANFIS). In this article we propose a method to use engineering experience and build a model, which can be used as a systematic decision support tool for engineers dealing with new problems. Planning geotechnical investigations is based on experience, which are used to obtain optimal number of investigation points, field and laboratory tests. To achieve this objective we define minimum number of investigation points and several input parameters which could increase or decrease the number of investigation points. The expert’s evaluations were put in a table, from which we generate the basis of the system. The paper presents a concept for planning geotechnical investigation for buildings using ANFIS and practical examples show its usefulness.

Discrete Optimization with Fuzzy Constraints

The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In this paper, heuristic fuzzy rules were used with the intention of improving the performance of optimization models, introducing experiential rules acquired from experts and utilizing recommendations. The aim of this paper was to define soft constraints using an adaptive network-based fuzzy inference system (ANFIS). This newly-developed soft constraint was applied to discrete optimization for obtaining optimal solutions. Even though the computational model is based on advanced computational technologies including fuzzy logic, neural networks and discrete optimization, it can be used to solve real-world problems of great interest for design engineers. The proposed computational model was used to find the minimum weight solutions for simply-supported laterally-restrained beams.

An Adaptive Network Fuzzy Inference System Approach for Site investigation

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.

NLP Optimization Model as a Failure Mechanism for Geosynthetic Reinforced Slopes Subjected to Pore-Water Pressure

AbstractThe majority of slope failures are triggered by excessive rainfall and the consequent increase in pore-water pressure within the slope. This paper presents the results of a computer code that quantifies earth pressure coefficients. This code is based on limit-equilibrium analyses and is used for the internal design of geosynthetic reinforced soil structures and to identify the critical failure mechanism. The critical failure mechanism is the largest value of out-of-balance force. For this purpose, the nonlinear programing (NLP) approach was used, and a NLP optimization model, TMAX, was developed. The model was used for failure mechanisms, assuming that the failure surfaces were bilinear. The influence of pore-water pressure on the potential failure surface was analyzed. The model was developed under basic principles. Optimally, the system is best suited for structures with varying geometries, different backfill unit weights, varying types of soil shear resistance, and different pore-water pressure...

Predicting Geotechnical Investigation Using the Knowledge Based System

The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System ANFIS was proposed. The obtained fuzzy inference system considers the influence of several entry parameters and computes the equivalent investigation ratio. The developed model ANFIS-SI can be applied to characterize any building site. The ANFIS-SI model takes into account project factors which are evaluated with a rating from 1 to 10. The model ANFIS-SI, with integrated recommendations can be used as a systematic decision support tool for engineers to evaluate the number of investigation points, field tests, and laboratory tests for a proper description of a building site. The determination of the optimal number of investigative points and the optimal number of each field test and laboratory test is presented on reference case.