Abstract
Based on differential quadrature method (DQM) and nonlocal strain gradient theory (NSGT), forced vibrations of a porous functionally graded (FG) scale-dependent beam in thermal environments have been investigated in this study. The nanobeam is assumed to be in contact with a moving point load. NSGT contains nonlocal stress field impacts together with the microstructure-dependent strains gradient impacts. The nano-size beam is constructed by functionally graded materials (FGMs) containing even and un-even pore dispersions within the material texture. The gradual material characteristics based upon pore effects have been characterized using refined power-law functions. Dynamical deflections of the nano-size beam have been calculated using DQM and Laplace transform technique. The prominence of temperature rise, nonlocal factor, strain gradient factor, travelling load speed, pore factor/distribution and elastic substrate on forced vibrational behaviors of nano-size beams have been explored.
Address
Raad M. Fenjan, Ridha A. Ahmed and Fatima Masood Hani: Al-Mustansiriah University, Engineering Collage P.O. Box 46049, Bab-Muadum, Baghdad 10001, Iraq
Nadhim M. Faleh: Ministry of construction and housing, Iraq
Abstract
Dynamic responses of porous piezoelectric and metal foam nano-size plates have been examined via a four variables plate formulation. Diverse pore dispersions named uniform, symmetric and asymmetric have been selected. The piezoelectric nano-size plate is subjected to an external electrical voltage. Nonlocal strain gradient theory (NSGT) which includes two scale factors has been utilized to provide size-dependent model of foam nanoplate. The presented plate formulation verifies the shear deformations impacts and it gives fewer number of field components compared to first-order plate model. Hamilton\'s principle has been utilized for deriving the governing equations. Achieved results by differential quadrature (DQ) method have been verified with those reported in previous studies. The influences of nonlocal factor, strain gradients, electrical voltage, dynamical load frequency and pore type on forced responses of metal and piezoelectric foam nano-size plates have been researched.
Key Words
response; piezoelectric; electric voltage; wave; piezoelectric plate
Address
Raad M. Fenjan, Ridha A. Ahmed and Fatima Masood Hani: 1Al-Mustansiriah University, Engineering Collage P.O. Box 46049, Bab-Muadum, Baghdad 10001, Iraq
Nadhim M. Faleh: Ministry of construction and housing, Iraq
Abstract
Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.
Key Words
structural health monitoring (SHM); structural temperature; deep learning; LSTM network; missing data recovery
Address
You-Liang Ding, Han-Wei Zhao and Man-Ya Wang: School of Civil Engineering, Southeast University, Nanjing 210096, China;
Key Laboratory of C&PC Structures of the Ministry of Education,
Southeast University, Nanjing 210096, China
Hao Liu: School of Civil Engineering, Southeast University, Nanjing 210096, China;
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR
Fang-Fang Geng: School of Architecture Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Abstract
Damage detection of structures is one of the most important topics in structural health monitoring. In practice, the response is not available at all structural degrees of freedom, and due to the installation of sensors at some degrees of freedom, responses exist only in limited number of degrees of freedom. This paper is investigated the damage detection of structures by applying two approaches, AllDOF and Dynamic Condensation Method (DCM), based on the Modified Modal Strain Energy Method (MMSEBI). In the AllDOF method, mode shapes in all degrees of freedom is available, but in the DCM the mode shapes only in some degrees of freedom are available. Therefore by methods like the DCM, mode shapes are obtained in slave degrees of freedom. So, in the first step, the responses at slave degrees of freedom extracted using the responses at master degrees of freedom. Then, using the reconstructed mode shape and obtaining the modified modal strain energy, the damages are detected. Two standard examples are used in different damage cases to evaluate the accuracy of the mentioned method. The results showed the capability of the DCM is acceptable for low mode shapes to detect the damage in structures. By increasing the number of modes, the AllDOF method identifies the locations of the damage more accurately.
Key Words
damage identification; modal strain energy; dynamic condensation method; Model reduction method
Address
Shahin Lale Arefi and Amin Gholizad: Department of Civil Engineering, University of Mohaghegh Ardabili, P.O. Box 56199-11397, Ardabil, Iran
Abstract
This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.
Address
Md Riasat Azim: Department of Civil & Environmental Engineering, University of Alberta, Natural Resources Engineering Facility 5-090, 9105 116 Street NW, Edmonton, Alberta, T6G 2W2, Canada
Haiyang Zhang: Department of Civil & Environmental Engineering, University of Alberta, Natural Resources Engineering Facility 5-042, 9105 116 Street NW, Edmonton, Alberta, T6G 2W2, Canada
Mustafa Gül: 3Department of Civil & Environmental Engineering, University of Alberta, Donadeo Innovation Centre for Engineering 7-257, 9211 116 Street NW, Edmonton, Alberta, T6G 1H9, Canada
