Engineering Computations: International Journal for Computer-Aided Engineering and Software

M.E. Díaz‐Fernández; C. González‐Nicieza; M.I. Álvarez‐Fernández; A.E. Álvarez‐Vigil; A. Argüelles Amado

doi: 10.1108/02644401011073656pmid: N/A

Purpose – This paper aims to present a computational approach which – setting off from measures obtained by using an overdrilling method – determines, automatically and accurately, stress changes undergone in terrain as a consequence of human activity. Design/methodology/approach – The method presented uses the data from three boreholes and the elasticity theory to represent a numerical system whose resolution allows determining the stress state in a particular point. Since the system obtained is over‐dimensioned, the Levenberg‐Marquardt minimization method has been used in order to minimize errors. This paper details the analysis carried out in order to develop the computational method. Findings – This paper provides the algorithm for determining inner stresses in a particular point of a rock mass. Besides, a method to verify obtained results is presented, including its computational encoding in C#. Furthermore, the developed methods have been integrated in a computer tool which presents the results in a graphic environment. Research limitations/implications – The algorithms presented are applicable when using an overdrilling method to measure stresses. Practical implications – A reliable determination of global stress state demands the use of any method that is numerically difficult to use. Thus, in practice, it is of great importance to dispose of some reliable automatic tool to calculate stress state. Originality/value – Accuracy in the results obtained with the tool, together with the simplicity of its interface, involves a certain advantage regarding the use of a general‐scope commercial tool, since it allows – without being necessary to be an expert user – quickly obtaining results within the analysed working area.

Wei‐Ling Chiang; Dung‐Jiang Chiou; Cheng‐Wu Chen; Jhy‐Pyng Tang; Wen‐Ko Hsu; Te‐Yu Liu

doi: 10.1108/02644401011073665pmid: N/A

Purpose – This study aims to investigate the relationship between structural damage and sensitivity indices using the Hilbert‐Huang transform (HHT) method. Design/methodology/approach – The relationship between structural damage and the sensitivity indices is obtained by using the HHT method. Three sensitivity indices are proposed: the ratio of rotation (RR), the ratio of shifting value (SV) and the ratio of bandwidth (RB). The nonlinear single degree of freedom and multiple degree of freedom models with various predominant frequencies are constructed using the SAP2000 program. Adjusted PGA El Centro and Chi‐Chi (TCU068) earthquake data are used as the excitations. Next, the sensitivity indices obtained using the HHT and the fast Fourier transform (FFT) methods are evaluated separately based on the acceleration responses of the roof structures to earthquakes. Findings – Simulation results indicate that, when RR < 1, the structural response is in the elastic region, and neither the RB nor SV in the HHT and FFT spectra change. When the structural response is nonlinear, i.e. RR1, a positive trend of change occurs in RB and RR, while in the HHT spectra, SV increases with an increasing RR. Moreover, the FFT spectra reveal that SV changes only when the RR is sufficiently large. No steady relationship between the RB and the RR can be found. Originality/value – The paper demonstrates the effectiveness of the HHT method.

Nikos D. Lagaros; Vagelis Plevris; Manolis Papadrakakis

doi: 10.1108/02644401011073674pmid: N/A

Purpose – This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability‐based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi‐objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized. Design/methodology/approach – The solution of the optimization problem is performed with the non‐dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real‐world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented. Findings – The obtained estimates with the neural network predictions are shown to be very satisfactory in terms of accuracy for performing this type of computation. Furthermore, the present numerical results manage to achieve a reduction in computational time up to four orders of magnitude, for low probabilities of violation, compared to the conventional procedure making thus feasible the reliability‐robust design optimization of realistic structures under probabilistic constraints. Originality/value – The novel parts of the present work include the implementation of neurocomputing strategies in RRDO problems for reducing the computational cost and the comparison of the results given by RRDO and robust design optimization formulations, where the significance of taking into account probabilistic constraints is emphasized.

R. Chowdhury; S. Adhikari

doi: 10.1108/02644401011073683pmid: N/A

Purpose – High‐dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for capturing the high‐dimensional relationships between sets of input and output model variables. It is an efficient formulation of the system response, if higher‐order cooperative effects are weak, allowing the physical model to be captured by the lower‐order terms. The paper's aim is to develop a new computational tool for estimating probabilistic sensitivity of structural/mechanical systems subject to random loads, material properties and geometry. Design/methodology/approach – When first‐order HDMR approximation of the original high‐dimensional limit state is not adequate to provide the desired accuracy to the sensitivity analysis, this paper presents an enhanced HDMR (eHDMR) method to represent the higher‐order terms of HDMR expansion by expressions similar to the lower‐order ones with monomial multipliers. The accuracy of the HDMR expansion can be significantly improved using preconditioning with a minimal number of additional input‐output samples without directly invoking the determination of second‐ and higher‐order terms. As a part of this effort, the efficacy of HDMR, which is recently applied to uncertainty analysis, is also demonstrated. The method is based on computing eHDMR approximation of system responses and score functions associated with probability distribution of a random input. Surrogate model is constructed using moving least squares interpolation formula. Once the surrogate model form is defined, both the probabilistic response and its sensitivities can be estimated from a single probabilistic analysis, without requiring the gradients of performance functions. Findings – The results of two numerical examples involving mathematical function and structural/solid‐mechanics problems indicate that the sensitivities obtained using eHDMR approximation provide significant accuracy when compared with the conventional Monte Carlo method, while requiring fewer original model simulations. Originality/value – This is the first time where application of eHDMR concepts is explored in the stochastic sensitivity analysis. The present computational approach is valuable to the practical modelling and design community.

Hong‐jun Li; Zhi‐min Zhao; Xiao‐lei Yu

doi: 10.1108/02644401011073692pmid: N/A

Purpose – The traditional total variation (TV) models in wavelet domain use thresholding directly in coefficients selection and show that Gibbs' phenomenon exists. However, the nonzero coefficient index set selected by hard thresholding techniques may not be the best choice to obtain the least oscillatory reconstructions near edges. This paper aims to propose an image denoising method based on TV and grey theory in the wavelet domain to solve the defect of traditional methods. Design/methodology/approach – In this paper, the authors divide wavelet into two parts: low frequency area and high frequency area; in different areas different methods are used. They apply grey theory in wavelet coefficient selection. The new algorithm gives a new method of wavelet coefficient selection, solves the nonzero coefficients sort, and achieves a good image denoising result while reducing the phenomenon of “Gibbs.” Findings – The results show that the method proposed in this paper can distinguish between the information of image and noise accurately and also reduce the Gibbs artifacts. From the comparisons, the model proposed preserves the important information of the image very well and shows very good performance. Originality/value – The proposed image denoising model introducing grey relation analysis in the wavelet coefficients selecting and modifying is original. The proposed model provides a viable tool to engineers for processing the image.

Alireza Ahangar‐Asr; Asaad Faramarzi; Akbar A. Javadi

doi: 10.1108/02644401011073700pmid: N/A

Purpose – Analysis of stability of slopes has been the subject of many research works in the past decades. Prediction of stability of slopes is of great importance in many civil engineering structures including earth dams, retaining walls and trenches. There are several parameters that contribute to the stability of slopes. This paper aims to present a new approach, based on evolutionary polynomial regression (EPR), for analysis of stability of soil and rock slopes. Design/methodology/approach – EPR is a data‐driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures. Findings – EPR models are developed and validated using results from sets of field data on the stability status of soil and rock slopes. The developed models are used to predict the factor of safety of slopes against failure for conditions not used in the model building process. The results show that the proposed approach is very effective and robust in modelling the behaviour of slopes and provides a unified approach to analysis of slope stability problems. It is also shown that the models can predict various aspects of behaviour of slopes correctly. Originality/value – In this paper a new evolutionary data mining approach is presented for the analysis of stability of soil and rock slopes. The new approach overcomes the shortcomings of the traditional and artificial neural network‐based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.