NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigatio

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Damage detection in structural health monitoring using combination of deep neural networks

Structural Health Monitoring is a process of continuous evaluation of infrastructure status. In order to be able to detect the damage status, data collected from sensors have to be processed to identify the difference between the damaged and the undamaged states. In recent years, convolution neural network has been applied to detect the structural damage and with positive results. This paper proposes a new method of damage detection using combination of deep neural networks. The method uses a convolution neural network to extract deep features in time series and Long Short Term Memory network to find a statistically significant correlation of each lagged features in time series data. These two types of features are combined to increase discrimination ability compared to deep features only. Finally, the fully connected layer will be used to classify the time series into normal and damaged states. The accuracy of damaged detection was tested on a benchmark dataset from Los Alamos National Laboratory and the result shows that hybrid features provided a highly accurate damage identification

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

An experimental study and a proposed theoretical solution for the prediction of the ductile/brittle failure modes of reinforced concrete beams strengthened with external steel plates

An experimental study and a proposed theoretical solution are conducted in the present study to investigate the ductile/brittle failure mode of reinforced concrete beams strengthened with an external steel plate. The present experimental study has fabricated and tested six steel plate-strengthened RC beams and one non-strengthened RC beam under 4-point bending loads. The proposed theoretical model is then developed based on the observed experimental results to analyze the crack formation, to determine the distance between vertical cracks and to quantitatively predict the ductile/brittle failure mode of plate-strengthened RC beams. The experimental study shows that the failure mode is based on the sliding of concrete along with the external plate. This slip is limited between two vertical cracks, from which the maximum stress in the external steel is determined. Based on comparisons conducted in the present study, excellent agreements of the stresses/strains in soffit steel plates, crack distances, and system failure modes between the current theoretical solution and the previous and present experimental results are observed.&nbsp

The Role of Cultural and Institutional Distances in International Trade

Despite the effectiveness of the observed barriers such as taxes and quotas to adjust bilateral trade, they are still not well supported by governments in general and the World Trade Organization in particular. Therefore, in recent years, unobserved barriers have been critical tools to modify the trade flows between nations worldwide. China’s exports account for a massive proportion of global trade. However, the role of cultural and institutional distance in China’s trade flow has not been much explored. This study analyzes the impact of cultural and institutional differences on China's exports between 2006-2017 by adopting a system-GMM estimator. The main findings are, first, that cultural and institutional differences between China and its trading partners reduce China's exports. Second, cultural and institutional distances have the strongest influence on China's exports to high-income countries, followed by low-income countries, and finally middle-income countries. Third, manufactured products are the most sensitive to cultural and institutional distances. Based on these findings, several policies for China, as well as for emerging economies in general, are suggested for reducing cultural and institutional distances and boosting their exports. Doi: 10.28991/ESJ-2023-07-02-015 Full Text: PD

Dynamic response analysis of truss bridges under the effect of moving vehicles

With the characteristics of heavy and concentrated loads, the influence of moving loads on the dynamic response of the bridges is significant. Therefore, in this paper, the dynamic response of a large-scale truss bridge is studied to consider the effect of the various parameters of moving loads. The considered main parameters consist of moving mass, moving velocity, and type of moving loads. The nonlinear dynamics of the bridge based on time history analysis are obtained using the Wilson-  method. four time history – based dynamic analysis method including modal superposition in frequency domain, modal superposition in time domain; direct time integration, and direct solution in the frequency domain are employed to analysis the obtained results. To compare the effectiveness of the aforementioned method. A large-scale railway truss bridge is employed for dynamic response analysis. The obtained results give more insight into the nature of the problem and help to determine the significant parameters of moving load affecting the bridge response

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM