As transport networks become more congested, and new highway construction recedes as a sustainable long-term solution, there is a growing need to adopt policies
As transport networks become more congested, there is a growing need to adopt policies that manage demand and make full use of existing assets. Advances in
Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.
In this work, the Convolutional Neural Network (CNN) algorithm is introduced in pipeline surface cracks monitoring-based image processing method for improving the efficiency and accuracy of crack type, location, and area identification.The method is used to extract the cracks area called the CNN based on crack contour network (CCN-CNNs) method from locate and extract the crack shape.CCN-CNNs is provides the accuracy rate (P%), recall rate (R%), and F-score (F%) index to assess the algorithm in the problem while identifying the cracks, and then according to the maximum F-score, we computes the crack corresponding contour area.In this work the pipeline crake images datasets are provided using an inspection drone with high definition camera.To the best of the authors' knowledge, the methodology presented in this paper for pipeline crack identification is an original contribution to the literature.This work introduces an efficient approach that also significantly reduces the time for crack type, location, and area identification of pipelines, the accuracy rate (P%), recall rate (R%), and Fscore (F%) are recorded 91.8% ,86.1%, and 84.6% respectively.
The optimization of the acoustic silencer volume is very important to develop it and to get high-performance, the importance of the silencer was appeared in industrial field to eliminate the noise of the duct by efficient and... | Find, read and cite all the research you need on Tech Science Press
The objective of this chapter is to provide the target readers with different theories of failure for the effective design of mechanical parts. Depending on the mechanical properties of the raw materials and the applications of the mechanical parts or structure, this chapter also discusses the selection of a particular theory for design, such as maximum shear stress (MSS), distortion energy (DE), maximum normal stress (MNS), brittle Coulomb-Mohr (BCM), or modified Mohr (MM), along with examples and solved/unsolved problems. This chapter can be used as teaching material for machine design courses for mechanical, industrial engineering, and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
Strain is sensitive to damage, especially in steel structures. But traditional strain gauge does not fit bridge damage identification because it only provides the strain information of the point where it is set up. While traditional strain gauges suffer from its drawbacks, long-gage FBG strain sensor is capable of providing the strain information of a certain range, which all the damage information within the sensing range can be reflected by the strain information provided by FBG sensors. The wavelet transform is a new way to analyze the signals, which is capable of providing multiple levels of details and approximations of the signal. In this paper, a wavelet packet transform-based damage identification is proposed for the steel bridge damage identifications numerically and with experimental experiment to validate the proposed method. The strain data obtained via long-gage FBG strain sensors are transformed into a modified wavelet packet energy rate index first to identify the location and severity of damage. The results of numerical simulations show that the proposed damage index is a good candidate which is capable of identifying both the location and severity of damage under noise effect.
The objective of this study is to predict the fatigue life of carbon fibre/epoxy composite laminate sheets involving 12 balanced woven bidirectional layers with the same orientation angle [0/90°]. The composite sheets considered are subjected to variable amplitude block loadings with different negative and positive stress ratios. This objective is accomplished by designing an efficient artificial neural network (ANN) architecture, with taking into account effect of the residual strength from spectrum loading. The number of cycles to failure (N) is related to the residual strength of the structure for constant amplitude loading. A simple first order model is postulated that determines the residual strength at any point during the fatigue life as a function of the static strength and stress ratio by applying the two-parameter Weibull probability density distribution. Two neural network structures, a feed-forward neural network (FFNN) and a radial basis neural network (RBNN), are applied, trained and tested to predict the fatigue life based on four groups of data considered. These data include the maximum stress (σmax) and the stress ratio (R), the Smith-Watson-Topper (SWT) parameter, fatigue strength ratio (Ψ), or failure criterion with the fibre orientation. On the other hand, the validity of the SWT, Ψ and selected suitable failure criterion for present study including material, loading and orientation were checked and modified before they were used for training the designed ANNs. The results show improvement when using one input data (SWT, or Ψ, or failure criterion) instead of two input data (σmax and R) and for the case of one input data the best prediction is observed for failure criterion condition, followed by Ψ and SWT respectively. Moreover, the RBNN demonstrates better results as compared with those obtained by the FFNN.
Machine parts are used under load, which includes static loads of constant intensity and variable loads of varying magnitude. In most cases, they are subject to variable loads. In addition, the accumulation of repeated loads is known to lead to fatigue, which causes mechanical parts to break at stresses much smaller than the breaking stress induced by the original static load. So when designing mold parts with short molding cycles and high injection times, maintenance costs can be reduced by taking measures to increase the fatigue limit. This chapter examines fatigue failure resulting from variable loading, along with examples and solved/unsolved problems, and includes topics on dynamic behavior of materials, fatigue behavior, fatigue failure, fatigue load, variable (dynamic) load, residual stress, stress concentration, fatigue strength, endurance limit, fatigue variables, fatigue analysis, and fatigue life. This chapter can be used as teaching material for courses on machine design for mechanical and industrial engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
As mentioned, transportation systems have become a fundamental base for the economic growth of all nations. Nevertheless, many countries around the world are
The four tank system is a widely used mechatronic laboratory system in control theory. This work is aimed to choose the best controller for the four tank system (4TS) with two input force. The optimal control is one of the best techniques in a sense of performance, and is demonstrated for the level control of 4TS. There are several controller systems in optimal control for this purpose which are Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian Regulator (LQGR), H2, and H∞ controller system. These controllers will be applied to this important mechatronic system (4TS) separately, and compared the performance for disturbance rejection with each other to study the effect of these controller systems on the 4TS controlled state. On the other hand the performances of the optimal control systems are compared with other controller performances available in literatures for the same case study. The results indicate that the Linear Quadratic Regulator (LQR) provides significant improvement over completely controllers. The simulations were carried out in MATLAB-Simulink.
Monitoring the mass of liquid absorption in laminated composite structures, that have a direct contact surface with working liquid-like pipes, is very important in order to prevent the sudden collapse of the structures because of the degradations in strength and mechanical properties over time. An electrical capacitance sensor technique has been applied for monitoring the mass of liquid absorption, over the time, in laminated composite pipelines by measuring the change of dielectric characteristics of composite pipelines subjected to an internal hydrostatic pressure load of water and thermal effect. Results show the technique is very effective. However, a major difficulty in utilizing this technique is that it is highly time consuming to be used for monitoring, in addition to the detection efforts that it requires to calculate the mass of liquid absorption that exerts high cost and loss of additional time in monitoring. In this paper, a deep neural network model is used to estimate the mass of liquid absorption in glass fiber reinforced epoxy laminated composite pipelines by extracting the features from datasets from experimental and numerical measurements of the electrical capacitance sensor. The experimental and numerical data used in this paper to train and test the new deep neural network model are collected from the literature and the finite element model of the electrical capacitance sensor system respectively. The results show an excellent agreement between the finite element model data, available experimental data, and those predicted by a deep neural network with an average error of 0.067%, and show that the proposed method achieves satisfactory performance with 86.34% accuracy, 82.83% regression rate and 83.74% F-score. The proposed approach overcomes the difficult problem of saving time and effort to accurately detect the mass of liquid absorption over the time, and provides a promising approach for a wider application of this intelligent model.
Smart cities need smart transport services. Proper movement of people, goods, and services accelerates the growth and development of a region. A well-planned
In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%).
