A novel regression prediction model for structural engineering applications

Jeng-Wen Lin,Cheng-Wu Chen,Ting-Chang Hsu 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.5

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.

Factor-analysis based questionnaire categorization method for reliability improvement of evaluation of working conditions in construction enterprises

Jeng-Wen Lin,Pu Fun Shen 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.6

This paper presents a factor-analysis based questionnaire categorization method to improve the reliability of the evaluation of working conditions without influencing the completeness of the questionnaire both in Taiwanese and Chinese construction enterprises for structural engineering applications. The proposed approach springs from the AI application and expert systems in structural engineering. Questions with a similar response pattern are grouped into or categorized as one factor. Questions that form a single factor usually have higher reliability than the entire questionnaire, especially in the case when the questionnaire is complex and inconsistent. By classifying questions based on the meanings of the words used in them and the responded scores, reliability could be increased. The principle for classification was that 90% of the questions in the same classified group must satisfy the proposed classification rule and consequently the lowest one was 92%. The results show that the question classification method could improve the reliability of the questionnaires for at least 0.7. Compared to the question deletion method using SPSS, 75% of the questions left were verified the same as the results obtained by applying the classification method.

Modeling and assessment of VWNN for signal processing of structural systems

Jeng-Wen Lin,Tzung-Han Wu 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.1

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

Mode-by-mode evaluation of structural systems using a bandpass-HHT filtering approach

Jeng-Wen Lin 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.36 No.6

This paper presents an improved version of the Hilbert-Huang transform (HHT) for the modal evaluation of structural systems or signals. In this improved HHT, a well-designed bandpass filter is used as preprocessing to separate and determine each mode of the signal for solving the inherent modemixing problem in HHT (i.e., empirical mode decomposition, EMD, associated with the Hilbert transform). A screening process is then applied to remove undesired intrinsic mode functions (IMFs) derived from the EMD of the signal’s mode. A “best” IMF is selected in each screening process that utilizes the orthogonalization coefficient between the signal’s mode and its IMFs. Through mode-by-mode signal filtering, parameters such as the modal frequency can be evaluated accurately when compared to the theoretical value. Time history of the identified modal frequency is available. Numerical results prove the efficiency of the proposed approach, showing relative errors 1.40%, 2.06%, and 1.46%, respectively, for the test cases of a benchmark structure in the lab, a simulated time-varying structural system, and of a linear superimposed cosine waves.

Design of Uncertain Multi-input Systems with State Delay and Input Deadzone Nonlinearity via Sliding Mode Control

Wen-Jeng Liu,Kuo-Kai Shyu,Kou-Cheng Hsu 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.3

The problem of robust stabilization of a class of uncertain multi-input time-delayed systems with deadzone nonlinearity in the actuator is considered. To achieve a stable uncertain multi-input system, sliding mode control (SMC) is adopted in the controller design. The proposed controller guarantees the global reaching condition of the sliding mode in the uncertain multi-input system. In the sliding mode, the investigated time-delayed systems with deadzone nonlinearity still possess the insensitivity to the uncertainties and/or disturbances, which can be seen in the systems with linear inputs. In addition, the proposed controller can work effectively for systems no matter whether sector nonlinearity and/or deadzone exists in the actuator or not. However, such property cannot be obtained by the controller design through traditional SMC for the systems without input nonlinearity. Besides, the traditional SMC controller might produce limit cycles once the system contains deadzone in the input. Furthermore, the presented controller ensures the system trajectories globally exponentially converged in the sliding mode. Finally, two examples are illustrated to demonstrate the effectiveness of the pro-posed sliding mode controller.

Repetitive model refinement for structural health monitoring using efficient Akaike information criterion

Jeng-Wen Lin 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.5

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

The Effect of Buoyancy on Laminar Air Flow Past a Rectangular Cavity

Wen Jeng Chang,Chiang Hua Fan 대한기계학회 1993 International Symposium on Transport Phenomena in Vol.4 No.1

A hybrid algorithm based on EEMD and EMD for multi-mode signal processing

Jeng-Wen Lin 국제구조공학회 2011 Structural Engineering and Mechanics, An Int'l Jou Vol.39 No.6

This paper presents an efficient version of Hilbert-Huang transform for nonlinear nonstationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the “best” outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

A novel regression prediction model for structural engineering applications

Lin, Jeng-Wen,Chen, Cheng-Wu,Hsu, Ting-Chang Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.5

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.

Factor-analysis based questionnaire categorization method for reliability improvement of evaluation of working conditions in construction enterprises

Lin, Jeng-Wen,Shen, Pu Fun Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.6

This paper presents a factor-analysis based questionnaire categorization method to improve the reliability of the evaluation of working conditions without influencing the completeness of the questionnaire both in Taiwanese and Chinese construction enterprises for structural engineering applications. The proposed approach springs from the AI application and expert systems in structural engineering. Questions with a similar response pattern are grouped into or categorized as one factor. Questions that form a single factor usually have higher reliability than the entire questionnaire, especially in the case when the questionnaire is complex and inconsistent. By classifying questions based on the meanings of the words used in them and the responded scores, reliability could be increased. The principle for classification was that 90% of the questions in the same classified group must satisfy the proposed classification rule and consequently the lowest one was 92%. The results show that the question classification method could improve the reliability of the questionnaires for at least 0.7. Compared to the question deletion method using SPSS, 75% of the questions left were verified the same as the results obtained by applying the classification method.