Self-constructing wavelet neural network algorithm for nonlinear control of large structures

Wang, Nengmou; Adeli, Hojjat

doi:10.1016/j.engappai.2015.01.018

Cited by 89 publications

(43 citation statements)

References 73 publications

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“…Recently, among different ML techniques, artificial neural network (ANN) has widely been used in civil engineering (Adeli & Panakkat, ; Panakkat & Adeli, ; Wang & Adeli, ). ANN was originally inspired by human brains in which information is transmitted and processed by biological neurons to build complicated concepts and ideas (Adeli, ).…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

216

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The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high‐order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross‐entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C‐ANN) and second‐order artificial neural network (SO‐ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high‐performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C‐ANN and SO‐ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water‐to‐cement and sand‐to‐cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

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Section: Introductionmentioning

confidence: 99%

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Nguyen

Kashani

Ngo

et al. 2018

Computer aided Civil Eng

216

View full text Add to dashboard Cite

show abstract

“…Using the Big Bang–Big Crunch algorithm to update the optimum values of the closed‐loop poles of the structural system on line, Amini and Zabihi Samani () present a time‐varying wavelet‐based pole assignment approach for the vibration control of the MDOF smart structures under earthquake. Wang and Adeli () present an adaptive robust control algorithm for nonlinear vibration control of large structures subjected to external excitations, where a self‐constructing wavelet neural network is developed specifically for functional approximation of the nonlinear behaviour of large structures, an adaptive fuzzy SMC is used to drive the actuator, and a fuzzy compensation controller is designed to minimize the chattering effect. The effectiveness of the method is demonstrated for vibration control of a continuous cast‐in‐place pre‐stressed concrete box‐girder bridge benchmark problem subjected to ground motions.…”

Section: Intelligent Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

Self Cite

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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“…Over the past few decades many active control algorithms have been developed such as the linear quadratic regulator (LQR) (Stavroulakis et al, 2006), linear quadratic Gaussian (LQG) (Wu and Yang, 2000), sliding mode control (SMC) (Alli and Yakut, 2005;Pai, 2010;Wang and Adeli, 2012, 2015a, 2015b, H 1 control (Yang et al, 1996), proportional-integral-derivative (PID) control (Kang et al, 2009), model predictive control (Wang et al, 2015), parallel control , and optimal control algorithm Saleh and Adeli, 1997, 1998a, 1998b. Saleh (1998, 1999) present an integrated control and optimization strategy for design of both civil structures and control system.…”

Section: Introductionmentioning

confidence: 99%

Self-constructing wavelet neural network algorithm for nonlinear control of large structures

Cited by 89 publications

References 73 publications

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

Control methodologies for vibration control of smart civil and mechanical structures

New discrete-time robust H2/H∞ algorithm for vibration control of smart structures using linear matrix inequalities

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