Brace diaphragm walls are commonly used in underground structures in metropolitan areas, where avoiding costly damage to adjacent infrastructure / buildings is critical to project success. It is necessary to make accurate diaphragm wall deflection predictions to ensure actual deflection falls within allowable limits, and thus ensure the safety of both the project and adjacent structures. Numerous studies and approaches, such as empirical, semi-empirical as well as numerical approaches, have addressed excavation-induced deflection in diaphragm walls. Artificial intelligence (AI) has been used recently by several researchers to improve diaphragm wall deflection prediction capabilities. This paper proposes a hybrid artificial intelligence system, namely the evolutionary fuzzy support vector machine inference model for time series data (EFSIMT ), to predict diaphragm wall deflection in deep excavation through the application of historical project data. Simulations were performed on 1,083 instances, segregated into a total of 988 training data sets and 95 test data sets. Validation results show that the EFSIMT achieves higher performance in comparison with Artificial Neural Networks and the Evolutionary Support Vector Machine Inference Model (ESIM). Therefore, EFSIMT has great potential as a predictive tool for diaphragm wall deflection problems and assisting project managers/engineers to ensure safety during the construction process.
Relations between two Boolean attributes derived from data can be
quantified by truth functions defined on four-fold tables corresponding to pairs of the attributes. Several classes of such quantifiers (implicational, double implicational, equivalence ones) with truth values in the unit interval were investigated in the frame of the theory of data mining methods. In the fuzzy logic theory, there are well-defined classes of fuzzy operators, namely t-norms representing various types of evaluations of fuzzy conjunction (and t-conorms representing fuzzy disjunction), and operators of fuzzy implications.
In the contribution, several types of constructions of quantifiers using fuzzy operators are described. Definitions and theorems presented by the author in previous contributions to WUPES workshops are summarized and illustrated by examples of well-known quantifiers and operators.
The paper deals with application of MF-ARTMAP neural network on
financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.
The article describes use of fuzzy logic in the evaluation of nonlimited values at measurement of lightning arresters in telecommunication networks. It describes the application of Γa-cut method and evaluates their electrical parametres and functional features based on the measurement of a selected sample of lightning arresters. and Článek popisuje využití fuzzy logiky v oblasti mimolimitních hodnot při měření bleskojistek nasazených v telekomunikačních sítích. Popisuje aplikaci metody αa-řezů, hodnotí elektrické parametry a funkční vlastnosti bleskojistek založené na měření vybraného vzorku.