In this study, the methodology and results of ambient vibration-based investigations of the historical Tash Mosque in Kosovo and a 3-story historical building in Bulgaria are presented. The investigations include full-scale in situ testing of both structures due to ambient vibrations induced by micro-seismic, wind, traffic, and other human activities. To this aim, Ranger seismometers and Kinemetric products were used. Measurements were performed in both horizontal directions in several points along the structures' height utilizing a high-speed data acquisition device. All recorded data have been analyzed and processed by the software developed at IZIIS, and then the processed data were used as input for modal analysis. The basic assumption is that the excitation can be considered as a stationary random process to have a relatively flat spectrum. The paper clearly describes the procedure used for investigations and presents the dynamic properties of the whole structures. The investigated structures are both historical buildings and defined as architectural heritage and the outcome of this study including the natural vibration frequencies and mode shapes) can be very benefi- cial for the verification stage of the analytical/numerical models for future retro- fitting/rehabilitation schemes.
With the rapid developments of global economy and construction technologies, bridge construction has experienced a rapid growth. On the other hand, long-span bridges are usually subjected to harsh and severe environment, including heavy traffic loads and corrosion. Thus the safety of long-span bridges is facing great challenge, causing the collapse of several bridges due to cable corrosion and truck overloading. In this study, first-passage probability of a long-span suspension bridge was evaluated considering actual heavy traffic loading. A random traffic flow load model was proposed by considering probabilistic characteristics of vehicles speed, vehicle to vehicle distances and vehicle weights, estimated based on the weigh-in-motion system of a highway bridge. Nanxi Yangtze River Bridge, which is a suspension bridge with mid-span length of 820 m, was selected as the prototype to investigate the traffic load effect. The variables such as the root-mean-square displacement and velocity of the bridge girders were evaluated. The first-passage reliability of the long-span bridge in 100 years was predicted. The numerical results can provide a theoretical basis for the traffic control and bridge maintenance during the service period.
This research investigates a damage identification framework for carbon fiber reinforced polymer composite pipeline systems using electrical capacitance sensors. First, a finite element pipeline model is established under the structural-thermal-electrostatic coupled field. Based on this model, an accumulative damage model is introduced to model the pipeline damage subjected to fatigue effect, and ten damage cases are considered. Both static and transient external excitations are loaded into the pipeline by installing distributed electrical capacitance sensors. A system transfer function is proposed and applied to the 'open loop' pipeline system. Results show these approaches perform great promises when damage evolves covering electrode pairs, the signal will change sensitively and abruptly with an order of magnitude.
To ensure the normal service of the bridge, it is necessary to detect and evaluate the health status of the bridge structure. This work provides a novel framework for damage detection in trusses bridges through analyzing of displacement sensors datasets and plotting...
The main goal of this paper is improving bridges structures health detection results to solve the problems of large errors of detection and poor efficiency of detection in the traditional models of detection. A bridge structural health monitoring (SHM) model based on...
A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate... | Find, read and cite all the research you need on Tech Science Press
This paper aims specifically at developing an efficient but low-cost methodology that can help detect early transportation infrastructure damages either by permanent or periodic monitoring. In this research, we used LiDAR scanning units (ground units fixed on holders and movable units fixed on UAV) integrated with a novel deep neural network (DNN) for disease monitoring of bridges. The monitoring model is based on a recurrent neural network with long short-term memory blocks (RNN-LSTM) since the LiDAR scanning datasets have a time-dependent and memory-dependent behavior. The results give high performance, in this way, the monitoring of a real lifeline can be analyzed by combining with the data from Li-DAR and DNN models.
Mechanical springs are common in engineering product designs and can be found in almost any engineering product, from consumer to heavy industrial. Disassemble anything that involves a mechanism, and chances are you will find some type of spring. This chapter examines the types of mechanical springs and covers the full design of springs subjected to static and dynamic loads, along with examples and solved/unsolved problems. The stress-strain analysis of spring types is covered, including helical compression springs and helical torsion springs. This chapter can be used as teaching material for courses in 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.
Previous broadband energy harvester techniques met many challenges like output power with a sharp peak, small enhancement in bandwidth, and large dimensions and weights. This paper introduces the Automatic Resonance Tuning (ART) technique of two piezoelectric beams to manage these challenges. The energy harvester of two clamped beams automatically adapts their natural frequencies corresponding to the ambient vibration using (sliding masses over the beams). The optimization using COMSOL was conducted to determine the frequency ranges of the low-frequency beam and high-frequency beam and maximize the output power. The bandwidth of the optimized ART harvester is broadened from 27 to 137 H z , ultra-broad bandwidth ( 110 H z ). Our Finite Element Method (FEM) results were validated with experimental results that exhibited excellent convergence. Usually, the dataset of voltage and power is collected by the FEM. Voltages and power evaluated using FEM for some positions are used as the convolutional neural network (CNN) input. CNN predicts the most of masses' positions over the harvester due to the complexity of repetition implementation FEM in several positions. Then, the CNNs are trained for new wide masses position prediction. The mean square error (MSE) of the training dataset is 2.5601 × 10 - 7 μ w and the performance of the CNN training is 97.62 % accuracy ( P % ), 95.38 % regression rate ( R % ), and 93.78 % F-score ( F % ), at epoch 1000 , which shows the effectiveness of the proposed approach.
The gear is one of the most common forms of mechanical transmission. Because of its smooth transmission and large transmission load, it is one of the transmission methods preferred by many engineers, although they may not know how to design gears from scratch, as most of them imitate existing gears and change the corresponding parameters. In this situation, either the strength is often insufficient, or it is over-designed, resulting in waste. This chapter provides gear design examples and shares the gear design process for spur, helical, bevel, and worm gear design, with the goal of providing greater understanding of gears. Examples and solved/unsolved problems for computational design of gears are provided. This chapter can be used as teaching material for courses in machine design for mechanical, industrial, 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.
In this research, the long-term tensile creep (LTTC) failure in basalt fiber reinforced polymer (BFRP) composites under ambient conditions was detected and predicted via an expert system, in order to monitor the LTTC of BFRP laminate composites. This was accomplished by using the electrical potential change (EPC) technique that employs an electrical capacitance sensor (ECS) in conjunction with an artificial neural network (ANN). A finite element (FE) simulation model for tensile creep detection is generated by ANSYS and MATLAB. Therefore, FE analyses are employed to obtain groups of data for the training of the ANNs. The proposed method is applied to minimize the number of FE analysis for keeping the cost down and save the time of the creep behavior monitoring to a minimum. The paper first presents a study on the creep monitoring for different levels of tensile creep (%σc) as a percentage of ultimate tensile strength (UTS) equal to (25%, 50% and 75%) using EPC technique. Subsequently, the trained ANN is utilized to predict the creep behavior for the level of %σc not included in the FE data. Four different values are selected for the level of %σc; (15, 35%, 60% and 85%).
In this paper, basic concepts of resilience, ecology and sustainability are introduced first. Then, associated performance metrics and interdependency of critical infrastructure systems are presented and discussed. Moreover, the importance of big data (BD) and data mining (DM), as emerging themes in this field, is discussed. Other relevant issues such as how to foster decision making and accountability to plan for any expansion in resilience services, resources, and the associated performance metrics and interdependency of critical infrastructure systems are presented. It is the recommendation of this study that due to the difficulty and complexity of resilience, and its definitional ambiguity, the ability to assess such a concept helps to bridge the gap between theory and application, and between the academic and the policy circles. A framework for creating resilient, ecological and sustainable infrastructure systems is also proposed, as a recommendation, in a more holistic and comprehensive way.
In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.
