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Applications in the Civil Engineering Domain
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
The domain of civil engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise, influenced by intuitions and past experiences of a designer. The conventional methods of computing that rely on analytical or empirical relations have become time-consuming and labor-intensive when posed with real-life problems. To study, model, and analyze such problems, approximate computer-based soft computing techniques inspired by the reasoning, intuition, consciousness, and wisdom possessed by human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims to exploit a given tolerance of imprecision; the trivial and uncertain nature of the problem yields an approximate solution to a problem in quick time. Soft computing is a multi-disciplinary field, using a variety of statistical, probabilistic, and optimization tools which complement each other, such as neural networks, genetic algorithms, fuzzy logic, and support vector machines.
Soft Computing
Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019
The primary considerations of traditional hard computing are precision, certainty, and rigor. In contrast, the principal notion in soft computing is that precision and certainty carry a cost; and that computation, reasoning, and decision making should exploit (wherever possible) the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for obtaining low-cost solutions. The corresponding facility in humans leads to the remarkable human ability to understand distorted speech, deciphering sloppy handwriting, comprehending the nuances of natural language, summarizing text, recognizing and classifying images, driving a vehicle in dense traffic, and, more generally, making rational decisions in an environment of uncertainty and imprecision. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation that lead to an acceptable solution at low cost. Soft computing is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost, by seeking for an approximate solution to an imprecisely or precisely formulated problem.
Intelligent Systems
Published in Yung C. Shin, Chengying Xu, Intelligent Systems Modeling, Optimization, and Control, 2017
In recent years, various soft computing based techniques have emerged as useful tools for solving various scientific and engineering problems that were not possible or convenient to handle by traditional methods. The soft computing techniques provide computationally efficient and yet effective means of modeling, analysis, and decision making of complex phenomena. In Wikipedia, soft computing is defined as “a collection of computational techniques in computer science, AI, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions.” The typical techniques that belong to the soft computing arena include artificial neural networks (ANNs), fuzzy sets and systems, evolutionary computation including evolutionary strategies (ESs), swarm intelligence and harmony search, Bayesian network, chaos theory, etc. Much of these soft computing techniques are inspired by biological processes or are the results of attempts to emulate such processes.
Prediction of cortical bone mineral apposition rate in response to loading using an adaptive neuro-fuzzy inference system
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Rakesh Kumar, Vimal Kumar Pathak
Thus, it is evident from literature that the mechanical loading and their parameters plays an important role in regulating and controlling the bone modelling parameters such as mineral apposition rate (MAR). Generally, in silico models (Chennimalai Kumar et al. 2012; Hambli and Rieger 2012; Tiwari et al. 2018) have considered daily stress/strain or strain energy density as a stimulus to create a correlation between site specific osteogenesis and physiological loading. However, these models fail to predict the amount of new bone formation at several instances as there was no generalized principle to corelate between mechanical loading parameters and bone modelling parameter i.e. MAR. Birkhold et al. (2016) reported that the cortical surfaces i.e. endosteal and periosteal surface experiences different bone remodelling rate. As a result, mineral apposition rate (MAR) i.e. amount of new bone formation differs at both the surfaces (periosteal and endosteal surfaces). Henceforth, a soft computing method is needed, that can predict the optimal loading configuration for maximum bone modelling parameter, such as mineral apposition rate at cortical surfaces. Typically, soft computing refers to use of computational techniques based on artificial intelligence that provide fast and lucrative solutions to non-trivial real-life problems for which analytical solutions (hard computing) are minimum or do not exist (Zadeh 1992).
Increasing the efficiency of vehicle ad-hoc network to enhance the safety status of highways by artificial neural network and fuzzy inference system
Published in Journal of Transportation Safety & Security, 2020
Hamid Behbahani, Amir Mohammadian Amiri, Navid Nadimi, David R. Ragland
Soft computing is an innovative way to create intelligent systems by bringing together human abilities for reasoning and learning in an indeterminate and uncertain situation. Fuzzy inference system (FIS) is used to systematically describe human knowledge and from it to infer and make the proper decision. In addition, it attempts to achieve a certain output based on imprecise terms similar to the way the human brain functions. The basic structure of FIS consists of three conceptual parts. The first part involves the rules, in the form of a series of if–then orders, which provide a combination of inputs and outputs. The second part is a database that defines the membership functions used in fuzzy rules. The third part is the mechanism that carries out the inference procedure using existing rules and facts to generate a reasonable output.
Forecasting stream flow using hybrid neuro-wavelet technique
Published in ISH Journal of Hydraulic Engineering, 2018
Shreenivas N. Londhe, Shweta Narkhede
One of the latest approaches for the development of systems possessing computational intelligence is soft computing approach. It attempts to integrate several different computing paradigms including artificial neural networks, fuzzy logic, and genetic algorithms to create ‘smart’ systems. Over the past two decades, neural networks are established as recognized tools that offer efficient and effective solutions for time series modeling and analyzing the behavior of complex dynamical systems along with simulating and forecasting hydrological applications. However, a problem with these and other linear and nonlinear methods is that they have limitations with non-stationary data. In the last decade, wavelet analysis has been investigated in a number of disciplines outside hydrology, and it has been found to be very effective with non-stationary data.