Automatic Pavement Crack Recognition Based on BP Neural Network

A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application

A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable

Displacement Prediction of Tunnel Surrounding Rock: A Comparison of Support Vector Machine and Artificial Neural Network

Displacement prediction of tunnel surrounding rock plays an important role in safety monitoring and quality control tunnel construction. In this paper, two methodologies, support vector machines (SVM) and artificial neural network (ANN), are introduced to predict tunnel surrounding rock displacement. Then the two modes are texted with the data of Fangtianchong tunnel, respectively. The comparative results show that solutions gained by SVM seem to be more robust with a smaller standard error compared to ANN. Generally, the comparison between artificial neural network (ANN) and SVM shows that SVM has a higher accuracy prediction than ANN. Results also show that SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction

Adaptive Driving Speed Guiding to Avoid Red Traffic Lights

Guiding (ADSG) concept, which can assist drivers to avoid red traffic lights and thus improve the driving efficiency. The functional workflow of ADSG is defined and a smartphone based system composition is designed. In order to figure out a legal, feasible and efficient guidance speed, 2 algorithms are designed, simulated and evaluated in the paper. After the evaluation a control strategy is raised, which combines the advantages of the 2 algorithms

Deterioration Prediction of Urban Bridges on Network Level Using Markov-Chain Model

Bridges play an important role in urban transportation network. However, it is hard to predict the bridge deterioration precisely in Shanghai, because records of various bridge types with different maintenance status coexist in the same database and the bridge age span is also large. Therefore a Markov-chain model capable of considering maintenance factors is proposed in this study. Three deterioration circumstances are modeled including natural decay, conventional recoverable decay, and enhanced recoverable decay. Three components as well as the whole bridge are predicted including bridge deck system, superstructure, and substructure. The Markov-chain model proposed can predict not only the distribution of the percentage of different condition rating (CR) grades on network level in any year but also the deterioration tendency of single bridge with any state. Bridge data records of ten years were used to verify the model and also to find the deterioration tendency of urban bridges in Shanghai. The results show that the bridge conditions would drop rapidly if no recoverable repair treatments were conducted. Proper repair could slow down the deterioration speed. The enhanced recoverable repair could significantly mitigate the deterioration process and even raise the CR grades after several years of maintenance and repair

Forecasting Dry Bulk Freight Index with Improved SVM

An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI

Automatic Pavement Crack Recognition Based on BP Neural Network

A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application

Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results

Twinning Behavior in Cold-Rolling Ultra-Thin Grain-Oriented Silicon Steel

Twinning behaviors in grains during cold rolling have been systematically studied in preparing ultra-thin grain-oriented silicon steel (UTGO) using a commercial glassless grain-oriented silicon steel as raw material. It is found that the twinning system with the maximum Schmid factor and shear mechanical work would be activated. The area fraction of twins increased with the cold rolling reduction. The orientations of twins mainly appeared to be α-fiber (//RD), most of which were {001} orientation. Analysis via combining deformation orientation simulation and twinning orientation calculation suggested that {001} oriented twinning occurred at 40–50% rolling reduction. The simulation also confirmed more {100} oriented twins would be produced in the cold rolling process and their orientation also showed less deviation from ideal {001} orientation when a raw material with a higher content of exact Goss oriented grains was used

Ratiometric Fluorescent Nanoprobe for Highly Sensitive Determination of Mercury Ions

In this study, a novel dual-emission ratiometric fluorescent nanoprobe (RFN) was synthesized and ultilized for highly sensitive determination of mercury ions. In this nanoprobe, fluorescein isothiocyanate (FITC) doped silica (SiO2) served as a reference signal, FITC–SiO2 microspheres were synthesized and modified with amino groups, and then Au Nanoclusters (AuNCs) were combined with the amino groups on the surface of the FITC–SiO2 microspheres to obtain the RFN. The selectivity, stability, and pH of the RFN were then optimized, and the determination of mercury ions was performed under optimal conditions. The probe fluorescence intensity ratio (F520 nm/F680 nm) and Hg2+ concentration (1.0 × 10−10 mol/L to 1.0 × 10−8 mol/L) showed a good linear relationship, with a correlation coefficient of R2 = 0.98802 and a detection limit of 1.0 × 10−10 mol/L, respectively. The probe was used for the determination of trace mercury ion in water samples, and the recovery rate was 98.15~100.45%, suggesting a wide range of applications in monitoring pollutants, such as heavy metal ion and in the area of environmental protection