A STUDY OF MACHINE LEARNING APPLICATIONS FOR SOLVING PROBLEMS IN STRUCTURAL ENGINEERING

Ph.D

Dynamic reliability analysis using the extended support vector regression (X-SVR)

© 2019 Elsevier Ltd For engineering applications, the dynamic system responses can be significantly affected by uncertainties in the system parameters including material and geometric properties as well as by uncertainties in the excitations. The reliability of dynamic systems is widely evaluated based on the first-passage theory. To improve the computational efficiency, surrogate models are widely used to approximate the relationship between the system inputs and outputs. In this paper, a new machine learning based metamodel, namely the extended support vector regression (X-SVR), is proposed for the reliability analysis of dynamic systems via utilizing the first-passage theory. Furthermore, the capability of X-SVR is enhanced by a new kernel function developed from the vectorized Gegenbauer polynomial, especially for solving complex engineering problems. Through the proposed approach, the relationship between the extremum of the dynamic responses and the input uncertain parameters is approximated by training the X-SVR model such that the probability of failure can be efficiently predicted without using other computational tools for numerical analysis, such as the finite element analysis (FEM). The feasibility and performance of the proposed surrogate model in dynamic reliability analysis is investigated by comparing it with the conventional ε-insensitive support vector regression (ε-SVR) with Gaussian kernel and Monte Carlo simulation (MSC). Four numerical examples are adopted to evidently demonstrate the practicability and efficiency of the proposed X-SVR method

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment

This paper presents a concise state-of-the-art review along with an exhaustive comparative investigation on surrogate models for critical comparative assessment of uncertainty in natural frequencies of composite plates on the basis of computational efficiency and accuracy. Both individual and combined variations of input parameters have been considered to account for the effect of low and high dimensional input parameter spaces in the surrogate based uncertainty quantification algorithms including the rate of convergence. Probabilistic characterization of the first three stochastic natural frequencies is carried out by using a finite element model that includes the effects of transverse shear deformation based on Mindlin’s theory in conjunction with a layer-wise random variable approach. The results obtained by different metamodels have been compared with the results of traditional Monte Carlo simulation (MCS) method for high fidelity uncertainty quantification. The crucial issue regarding influence of sampling techniques on the performance of metamodel based uncertainty quantification has been addressed as an integral part of this article

Kriging metamodeling-based Monte Carlo Simulation for improved seismic fragility analysis of structures

The polynomial response surface method (RSM) is mostly adopted to overcome computational challenge of Monte Carlo Simulation (MCS)-based seismic fragility analysis (SFA) of structure. However, such SFA approach is primarily based on dual RSM involving lognormal assumption which lacks desired accuracy. The present study explores the advantage of adaptive nature of Kriging approach for improved SFA by random selection of metamodel to implicitly consider record to record variations of earthquakes. Without additional computational burden, the approach avoids a prior distribution assumption unlike dual RSM. The effectiveness of the approach over the usual polynomial RSM for SFA is elucidated numerically

Machine Learning for Extracting Features of Approximate Optimal Brace Locations for Steel Frames

A method is presented for extracting features of approximate optimal brace types and locations for large-scale steel building frames. The frame is subjected to static seismic loads, and the maximum stress in the frame members is minimized under constraints on the number of braces in each story and the maximum interstory drift angle. A new formulation is presented for extracting important features of brace types and locations from the machine learning results using a support vector machine with radial basis function kernel. A nonlinear programming problem is to be solved for finding the optimal values of the components of the matrix for condensing the features of a large-scale frame to those of a small-scale frame so that the important features of the large-scale frame can be extracted from the machine learning results of the small-scale frame. It is shown in the numerical examples that the important features of a 24-story frame are successfully extracted using the machine learning results of a 12-story frame

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Practical identifiability analysis of environmental models

Identifiability of a system model can be considered as the extent to which one can capture its parameter values from observational data and other prior knowledge of the system. Identifiability must be considered in context so that the objectives of the modelling must also be taken into account in its interpretation. A model may be identifiable for certain objective functions but not others; its identifiability may depend not just on the model structure but also on the level and type of noise, and may even not be identifiable when there is no noise on the observational data. Context also means that non-identifiability might not matter in some contexts, such as when representing pluralistic values among stakeholders, and may be very important in others, such as where it leads to intolerable uncertainties in model predictions. Uncertainty quantification of environmental systems is receiving increasing attention especially through the development of sophisticated methods, often statistically-based. This is partly driven by the desire of society and its decision makers to make more informed judgments as to how systems are better managed and associated resources efficiently allocated. Less attention seems to be given by modellers to understand the imperfections in their models and their implications. Practical methods of identifiability analysis can assist greatly here to assess if there is an identifiability problem so that one can proceed to decide if it matters, and if so how to go about modifying the model (transforming parameters, selecting specific data periods, changing model structure, using a more sophisticated objective function). A suite of relevant methods is available and the major useful ones are discussed here including sensitivity analysis, response surface methods, model emulation and the quantification of uncertainty. The paper also addresses various perspectives and concepts that warrant further development and use