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Taking high performance concrete (HPC) in hydraulic engineering as an example, this paper discusses the influence of different mixing methods of slag powder and fly ash on its frost resistance and chloride ion permeability. The results show that the compressive strength decreases with the addition of HPC; Proper addition of mineral admixtures is beneficial to improve the anti chloride ion permeability of HPC and reduce the electric flux; The HPC flux of 28d and 90 reaches 680C and 280C, when the mass ratio of slag powder to fly ash is 15:35 and the content of mineral admixture is 35 %; The frost resistance of HPC gradually deteriorates with the increase of mineral admixture under the condition of constant air content and water binder ratio. The frost resistance grade of HPC without mineral admixture reaches F200, and its frost resistance is the best.

A covalent organic framework (COF)/manganese dioxide (MnO2) nanocomposite (COF/MnO2) was prepared via a green and facile method. Using an etched halloysite nanotube (eHNT) as a material on the sens...

Fingerprints formed by the raised papillary ridges are one of the most important markers for individual identification. However, the current visualization methods for latent fingerprints (LFPs) suffer from poor resolution, low contrast, and high toxicity. In this work, the CsPbBr3/Cs4PbBr6nanostructured composite crystal (CsPbBr3/Cs4PbBr6NCC) were synthesized via a simple chemical solvent-assisted method. Compared with conventional perovskites, the as-prepared CsPbBr3/Cs4PbBr6NCC present an outstanding long-term environmental and water stability with 42% and 80% photoluminescence intensity remaining after 28 d under water and air conditions, respectively. Moreover, a special response to biomolecules from fingerprints was observed due to the hydrophobic interactions between the CsPbBr3/Cs4PbBr6NCC surface hydrophobic ligands (oleyl amine and oleic acid) and the hydrophobic groups in the biomolecules from the human fingers. Clear LFPs images were visualized in a bright environment illuminating the prepared CsPbBr3/Cs4PbBr6NCC powder under UV light of wavelength 365 nm. The images were also obtained on porous and non-porous surfaces such as metal, plastic, wood, glass, and paper products. These perovskite nanocrystals are expected a stable and bright luminescent labeling agent for LFPs visualization and have potential application in crime scene and personal identifications.

This work describes a novel affinity peptide-antibody sandwich electrochemical strategy for the ultrasensitive detection of prostate-specific antigen (PSA). Herein, polydopamine-coated boron-doped carbon nitride (Au@PDA@BCN) was synthesized and used as a sensing platform to anchor gold nanoparticles and immobilize primary antibody. Meanwhile, AuPt metallic nanoparticle and manganese dioxide (MnO

Land transportation systems in a port city could contribute to the competitiveness of the port and the economic development of the city. However, in port cities the land transportation system, which includes many interrelated factors, is complex. A system dynamics approach was applied to analyze the land transportation system, which was divided into economy, transport network and transportation investment subsystems. Then, system dynamics models were built to simulate the interactive relationship between the land transportation system in a port city and the economy in the port. The proposed system dynamics models analyze the impact of different transportation levels and the proportion of road and railway investment on the land transportation systems in a port city. Finally, the research takes Tianjin city as an example for numerical analysis, and analyzes the impact of different combinations of transport investment on the land transportation system in a port city.

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.

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.

The current match method of electric powertrain still makes use of longitudinal dynamics, which can’t realize maximum capacity for on-board energy storage unit and can’t reach lowest equivalent fuel consumption as well. Another match method focuses on improving available space considering reasonable layout of vehicle to enlarge rated energy capacity for on-board energy storage unit, which can keep the longitudinal dynamics performance almost unchanged but can’t reach lowest fuel consumption. Considering the characteristics of driving motor, method of electric powertrain matching utilizing conventional longitudinal dynamics for driving system and cut-and-try method for energy storage system is proposed for passenger cars converted from traditional ones. Through combining the utilization of vehicle space which contributes to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement together, a comprehensive optimal match method of electric powertrain for battery-powered electric vehicle is raised. In simulation, the vehicle model and match method is built in Matlab/simulink, and the Environmental Protection Agency (EPA) Urban Dynamometer Driving Schedule (UDDS) is chosen as a test condition. The simulation results show that 2.62% of regenerative energy and 2% of energy storage efficiency are increased relative to the traditional method. The research conclusions provide theoretical and practical solutions for electric powertrain matching for modern battery-powered electric vehicles especially for those converted from traditional ones, and further enhance dynamics of electric vehicles.

To develop new in-wheel-motor electric vehicle with compact chassis structure, using a double-wishbone suspension mechanism with zero toe-in fluctuation, a module structure composed of double-wishbone suspension with torsion bar, sensor, brake and in-wheel motor was presented, and a new method for analyzing and designing the stiffness and damping parameters of double-wishbone suspension with torsion bar was strictly derived. Then, the theoretical results above were successfully applied in the chassis development of new electric vehicles with four in-wheel motors, such as fuel cell mini-car with steer-by-wire, etc.

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 ofFangtianchongtunnel, 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.

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.

Line-structured light pavement detection has been developed for about two decades, but it is not yet mature because of unstable measurement results. An image-processing procedure based on ROI (region of interest) images is proposed in this study, which includes slight center extraction, connection, smoothing, and the rutting parameter extraction. The light center extraction is the foundation of the entire process. Two extraction methods are discussed in this study, Gaussian method and the gravity method. The Gaussian method can find the light center curve precisely, but the calculation process is time consuming. The gravity method is based on the assumption that the light intensity of the laser line distributes uniformly along the cross-sections, which is a simplification of the Gaussian method and has a high processing efficiency. The light center curve extracted by Gaussian method is considered as the true value to be used to evaluate the accuracy of the gravity method. The result shows that the error introduced by simplification is acceptable, and the gravity method can be used in practice instead of the Gaussian method.

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.

Automatic pavement detection based on video or image acquisition has become a common means of modern pavement management. However, the effect of pavement image processing is not quite well understood due to large amounts of noise. The low portion of crack pixels to the entire image makes the situation even worse. In this research, an efficient way of image preprocessing was developed for pavement crack images. This study included three steps, background correction, Gaussian smoothing and histogram transformation. Actual pavement crack images were preprocessed by the new method and then segmented by Otsu method. The results show that the preprocessing steps presented in this study dramatically dampened the impact of noise on image segmentation and retained most of the distress details at the same time. The Otsu method provided good segmented crack images if the images have been properly preprocessed. In the case of the crack images tested in this study, the preprocessing effect in the line cracking images was good enough for the following segmentation, and the effect in the alligator cracking images was even better.

This paper presents an economic analysis model on value chain of taxi fleet with battery-swapping mode in a pilot city. In the model, economic benefits of charging-swapping station group, taxi company, and taxi driver in the region have been taken into consideration. Thus, the model is a multiobjective function and multiobjective genetic algorithm is used to solve this problem. According to the real data collected from the pilot city, the multiobjective genetic algorithm is tested as an effective method to solve this problem. Furthermore, the effects of price of electricity, price of battery package, life cycle of battery package, cost of battery-swapping devices and infrastructure, and driving mileage per day on the benefits of value holders are analyzed, which provide theoretical and practical reference for the deployment of electric vehicles, for the national subsidy criteria adjusment, technological innovation instruction, commercial mode selection, and infrastructure construction.

Considering the characteristics of driving motor, method of electric Powertrain system match used by passenger cars was proposed. Research results show that such kind of method can't get maximum capacity for on-board energy storage unit and can't reach lowest equivalent fuel consumption as well, which will shorten the driving range of the car. A new match method to enlarge rated energy capacity for on-board energy storage unit is put forth here, which expand the available space for on-board energy storage unit compared with the traditional Powertrain system match method, to get more energy storage unit and extend the driving range. Based on this two solutions listed above, a comprehensive match method for pure electric vehicle Powertrain system is raised based on summing up the utilization of vehicle space which contribute to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement. Research conclusions provide theoretical and practical solution for Powertrain system match for modern pure electric vehicle, and further complete dynamics of electric vehicles.

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.