Search

This study builds a large database from an extensive survey of existing bond strengths between ultrahigh-performance concrete (UHPC) and normal-strength concrete (NC), with total 563 specimens being collected. To make up for some factors not included in the existing tests, an additional 38 specimens were tested with the improved slant shear method to enrich the database. The four governing factors for the interface strength were identified from the test results of the total 563+38 specimens: compressive strengths of UHPC and NC materials, interface roughness, normal stress level, and casting sequence. It is highlighted that this work discusses the effect of casting sequence. To obtain an accurate prediction formula, an artificial neural network (ANN) model is constructed, where the effect of casting sequence was firstly introduced. Based on the trained ANN model, an explicit formula is presented that significantly minimizes the prediction error. A modified shear-friction formula is also proposed for the UHPC–NC interface shear strength. Compared with other calculation models for concrete interface strength, this proposed formula with multiple parameters has a better accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the problem on an unique equilibrium solution of the bidirectional associative memory neural networks with distributed time-varying delays is investigated via Lyapunov stability theory. By using topological degree theory and M-matrix theory, several sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of equilibrium solution are derived. An illustrative example is given to demonstrate the effectiveness of the obtained results. [Copyright &y& Elsevier]

The bond strength between ultra-high-performance concrete (UHPC) and normal-strength concrete (NC) plays an important role in governing the composite specimens' overall behaviors. Unfortunately, there are still no widely accepted formulas targeting UHPC–NC interfacial strength, either in their specifications or in research papers. To this end, this study constructs an experimental database, consisting of 563 and 338 specimens for splitting and slant shear tests, respectively. Moreover, an additional 35 specimens for "improved" slant shear tests were performed, which could circumvent concrete crushing and trigger interfacial debonding. Additionally, for the first time in our tests, the effect of casting sequence on UHPC–NC bond strength was identified. Based on the database, an artificial neural network (ANN) model is proposed with the following inputs: namely, the normal stress perpendicular to the interface, the interface roughness, and the compressive strengths of the UHPC and NC materials. Based on the ANN analyses, the explicit expression of UHPC–NC bond strength is proposed, which significantly lowers the prediction error. To be fully compatible with the specifications, the conventional shear-friction formula is modified. By splitting the total force into adhesion and friction forces, the modified formula additionally takes the casting sequence into account. Although sacrificing accuracy to some extent compared to the ANN model, the modified formula relies on a solid physical basis and its accuracy is enhanced significantly compared to the existing formulas in specifications or research papers. [ABSTRACT FROM AUTHOR]

In this paper, we explore various adaptive factors that can influence the emergence of a communication system that benefits the receiver of signals (the hearer) but not the emitter (the speaker). Using computer simulations of a population of interacting agents whose behaviour is determined by a neural network, we show that a stable communication system does not emerge in groups of unrelated individuals because of its altruistic character. None the less, another set of simulations shows that the emergence of a language that confers an advantage only to hearers, not to speakers, is possible under at least three conditions: (1) if the hearer and the speaker tend to share the same genes, as predicted by kin selection theory; (2) if the population is ‘docile’ and the communication system is culturally transmitted together with other adaptive behaviours, as predicted by Simon’s docility theory; and (3) if the linguistic system is used not only for social communication, but also for talking to oneself, in particular as an aid to memory. [ABSTRACT FROM AUTHOR]

The work aimed to determine the effectiveness of the treatment of wastewater generated by the meat industry in a hybrid system combining biological methods of activated sludge (in an SBR reactor) and reverse osmosis. The tests carried out on the wastewater from the meat processing plant Uni-Lang in Wrzosowa showed that the biological treatment resulted in sufficient removal of contaminants from the wastewater, which consequently could be discharged into receiving water. In order to make it possible for the wastewater to be reused in the production cycle, it was additionally treated with reverse osmosis. The research was described mathematically by the program MATLAB using artificial neural networks. The program enables a prediction of the results for the treatment of wastewater over a range of tested values. [Copyright &y& Elsevier]

We examine the applicability of Principal Component Analysis (PCA) and Artificial Neural Network (ANN) methods of data analysis to biosignature detection. These techniques show promise in classifying and simplifying the representation of patterns of amino acids resulting from biological and non-biological syntheses. PCA correctly identifies glycine and alanine as the amino acids contributing the most information to the task of discriminating biotic and abiotic samples. Trained ANNs correctly classify between 86.1 and 99.5% of a large set of amino acid samples as biotic or abiotic. These and similar techniques are important in the design of automated data analysis systems for robotic missions to distant planetary bodies. Both techniques are robust with respect to noisy and incomplete data. Analysis of the performance of PCA and ANNs also lends insight into the localization of useful information within a particular data set, a feature that may be exploited in the selection of experiments for efficient mission design. [Copyright &y& Elsevier]

This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2[sup kr]/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results. [ABSTRACT FROM AUTHOR]

Of all the issues discussed at Alife VII: Looking Forward, Looking Backward, the issue of whether it was possible to create an artificial life system that exhibits open-ended evolution of novelty is by far the biggest. Of the 14 open problems settled on as a result of debate at the conference, some 6 are directly, or indirectly related to this issue. Most people equate open-ended evolution with complexity growth, although a priori these seem to be different things. In this paper I report on experiments to measure the complexity of Tierran organisms, and show the results for a size-neutral run of Tierra. In this run, no increase in organismal complexity was observed, although organism size did increase through the run. This result is discussed, offering some signposts on path to solving the issue of open ended evolution. [ABSTRACT FROM AUTHOR]