Bio-signal Processing Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

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Comparison of Artificial Neural Network, Genetic Programming, and Mechanistic Modeling of Complex Biological Processes

Environmental Engineering Science ◽

10.1089/10928750152725998 ◽

2001 ◽

Vol 18 (5) ◽

pp. 267-278 ◽

Cited By ~ 1

Author(s):

Jim Chen ◽

Roger L. Ely

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Genetic Programming ◽

Mechanistic Modeling ◽

Biological Processes ◽

Artificial Neural

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Damage detection of structures using signal processing and artificial neural networks

Advances in Structural Engineering ◽

10.1177/1369433219886079 ◽

2019 ◽

Vol 23 (5) ◽

pp. 884-897 ◽

Cited By ~ 2

Author(s):

Seyed Bahram Beheshti Aval ◽

Vahid Ahmadian ◽

Mohammad Maldar ◽

Ehsan Darvishan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Signal Processing ◽

Damage Detection ◽

Empirical Mode Decomposition ◽

Degree Of Freedom ◽

Benchmark Problem ◽

Time Frequency ◽

Mode Decomposition ◽

Artificial Neural

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.

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