160t提梁机在T梁预制建设中的应用

主要从安全性能、高效性、建场配备设备费用、场区合理布置等四个主要方面,就提梁机模式和传统的横移梁模式2种不同的建场方案进行对比分析;结果显示提梁机模式建场方案具有较好的优势。     

Road traffic conditions in Kenya: Exploring the policies and traffic cultures from unstructured user-generated data using NLP

Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, we scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, we gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. We applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.     

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.     

Statistical and machine learning approach for planning dial-a-ride systems

Door-to-door transportation service for elderly and persons with disabilities is often called dial-a-ride (DAR), and is usually provided by transit agencies through private contractors. Growth in DAR ridership is reported across the United States and this tendency will likely continue due to aging population. Such trends encourage development of models that can provide decision support in planning new DAR systems or expanding existing ones. Several statistical models were previously developed to predict the required DAR system capacity, given various characteristics of the service region, level-of-service requirements and operator constraints. Our work contributes to this line of research by proposing statistical and machine learning approaches that provide more accurate predictions over a wider range of scenarios. This is accomplished through transformation of variables and application of generalized linear model and support vector regression. Proposed models are built into an online tool that can help transit planners and policy makers: (a) estimate the capacity and operating cost of a DAR system needed to provide the desired level of service, (b) explore tradeoffs between system costs and levels of service, and (c) compare the cost of providing DAR service with other transportation alternatives (e.g., taxi, conventional transit).     

水泥混凝土路面湿法泵喷特种砂浆快速修补机械与工艺的试验研究

通过对水泥混凝土路面快速修补机械与工艺的试验研究，研制出适用于水泥混凝土路面大面积罩面的砂浆湿法泵喷射机，并提出了与之配套的修补工艺。     

浅谈基桩完整性无损检测技术的工程应用

反射波法和机械阻抗法作为基桩完整性检测的基本方法。在基桩检测中,两种方法对比使用,可取长补短,有效排除干扰,能够准确、真实地反映基桩质量。     

插装阀在立式双孔拉床液压系统中的应用

分析了插装阀在机床液压系统中的应用依据,分析了原拉床液压系统的工作原理,用插装阀设计了立式双孔拉床机床的液压系统,很好地替代了原拉床的液压系统。分析了插装阀工作原理。在拉床液压系统中,采用插装阀具有许多优点,在技术和经济上可行,符合技术发展趋势。     

沥青混合料高温稳定性能评价方法

介绍和对比了三种沥青混合料高温稳定性能的评价方法.车辙试验应用广泛,其不足也比较明显;旋转压实剪切试验机（GTM）试验和单轴贯入试验能够简便快速地检测沥青混合料剪切性能,两者具有较高的相关性,可以作为评价高温稳定性能的有效手段.     

新能源汽车驱动电机性能研究

新能源汽车的驱动电机是车辆的核心部件,因为车辆的最高车速、加速时间、爬坡能力等整车性能,与驱动电机有着密切的关系。目前,国内外电动机的结构众多,性能不一,工作原理也不尽相同。本文着重以三相交流异步电动机为例,论述其构造、原理、性能等方面内容。     

基于支持向量机的物流量预测——以成都市为例

考虑到支持向量机强大的泛化能力,运用支持向量机技术预测了成都市近期和中期物流量.研究表明,随着样本数据的增加,支持向量机的预测值与实际值间的相对误差总体呈下降趋势,并且与多元线性回归方法相比,支持向量机具有较高的预测精度,证明了支持向量机用于物流量预测的有效性.