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1

Assessment of Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl) of rock aggregates using gene expression programming and artificial neural networks

Köken Ekin

Archives of Mining Sciences

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2022

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Vol. 67, no. 3

401--422

EN

It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (M wl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and M wl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and M wl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.

2

Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNS), and linear multivariate regression (LMR)

Shakeri Jamshid, Shokri Behshad Jodeiri, Dehghani Hesam

Archives of Mining Sciences

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2020

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Vol. 65, no. 2

317--335

EN

In this paper, an attempt was made to find out two empirical relationships incorporating linear mul-tivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.

3

Predicting and minimizing the blasting cost in limestone mines using a combination of gene expression programming and particle swarm optimization

Bastami Reza, Bazzazi Abbas Aghajani, Shoormasti Hadi Hamidian, Ahangari Kaveh

Archives of Mining Sciences

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2020

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Vol. 65, no. 4

835--850

EN

Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as frag-mentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole dia-meter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtainedas 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.

4

The On-line Evolutionary Method for Soft Fault Diagnosis in Diode-transistor Circuits

Korzybski M., Ossowski M.

International Journal of Electronics and Telecommunications

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2015

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Vol. 61, No. 1

109-115

EN

The paper is devoted to diagnostic method enabling us to perform all the three levels of fault investigations - detection, localization and identification. It is designed for analog diode-transistor circuits, in which the circuit’s state is defined by the DC sources’ values causing elements operating points and the harmonic components with small amplitudes being calculated in accordance with small-signal circuit analysis rules. Geneexpression programming (GEP), differential evolution (DE) and genetic algorithms (GA) are a mathematical background of the proposed algorithms. Time consumed by diagnostic process rises rapidly with the increasing number of possible faulty circuit elements in case of using any of mentioned algorithms. The conncept of using two different circuit models with partly different elements allows us to decrease a number of possibly faulty elements in each circuit because some of possibly faulty elements are absent in one of two investigated circuits.

5

Two-stage algorithm for soft fault diagnosis in analog dynamic circuits

Korzybski M.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 5

74-76

EN

The paper deals with the soft fault diagnosis in analog dynamic circuits. The two-stage algorithm for soft fault location and identification has been presented. It is based on the spectrum analysis of the circuit response to the rectangular input signal, a neural network and one of the new evolutionary techniques - gene expression programming. The first stage enable us fault location using neural network. The result of the second stage is fault identification performed with formulas derived using gene expression programming. The method is illustrated with a numerical example.

PL

Tematem pracy jest diagnostyka uszkodzeń parametrycznych w analogowych układach dynamicznych. Przedstawiony jest dwustopniowy algorytm lokalizacji oraz identyfikacji uszkodzeń parametrycznych bazujący na analizie widmowej odpowiedzi układu na prostokątny sygnał wejściowy. Pierwszy stopień realizuje lokalizację uszkodzenia wykorzystując sieć neuronową. Wynikiem drugiego jest identyfikacja, którą umożliwiają zależności wyznaczone przez ewolucyjny algorytm programowania wyrażeń genetycznych.

6

Fault diagnosis of analog circuits using evolutionary algorithms

Korzybski M.

Zeszyty Naukowe. Elektryka / Politechnika Łódzka

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2008

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z. 113

69-74

EN

In this paper new methods for detection, location and identification of multiple faults in linear and nonlinear analog circuits are described. Also a method for finding an optimal set of the test nodes for soft fault diagnosis in linear and nonlinear DC circuits is developed. The methods are based on the node approach and two variants of the evolutionary computing: the genetic algorithms and the gene expression programming.

PL

W pracy zostały przedstawione opracowane przez autora metody diagnostyki analogowych układów elektronicznych przeznaczone dla uszkodzeń parametrycznych, wykorzystujące ewolucyjne metody obliczeniowe. 1. Metoda detekcji, lokalizacji oraz identyfikacji wielokrotnych uszkodzeń parametrycznych dla liniowych i nieliniowych obwodów DC oraz obwodów AC wykorzystująca w procesie detekcji i lokalizacji koncepcje programowania liniowego, a w procesie identyfikacji algorytmy genetyczne. 2. Metoda optymalizacji zbioru węzłów testowych dla diagnostyki uszkodzeń parametrycznych wykorzystująca algorytmu genetyczne. 3. Słownikowa metoda detekcji, lokalizacji oraz identyfikacji wielokrotnych uszkodzeń parametrycznych dla liniowych i nieliniowych obwodów DC oraz obwodów AC wykorzystująca w procesie tworzenia słownika ewolucyjny algorytm: gene expression programming. Efektywność wszystkich metod została zweryfikowana w zamieszczonych w pracy przykładach.

7

Heuristic methods to test frequencies optimization for analogue circuits diagnosis

Jantos P., Grzechca D., Golonek T., Rutkowski J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2008

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Vol. 56, nr 1

29-38

EN

This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.

8

EMOT - an evolutionary approach to 3D computer animation

Kwaśnicka H., Woźniak P.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2007

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Vol. 11, No 1-2

71-86

EN

Key-framing and Inverse Kinematics are popular animation methods, but new approaches are still developed. We propose a new evolutionary method of creating animation - the EMOT (Evolutionary MOTion) system. It enables automation of motion of animated characters and uses a new evolutionary approach - Gene Expression Programming (GEP). Characters are controlled by computer programs, an animator providing the way of motion's evaluation. GEP works with a randomly selected initial population, using directed but random selection. Experiments have shown that the proposed method is capable of developing robust controllers.