公路交通基础设施之施工技术管理与发展方向研究

以系统论为指导思想,将施工技术与工程管理相结合,科学地将施工技术管理体系分解为15个板块,分析了各板块的内容,提出了道路、桥梁及隧道专业施工技术发展方向及工法分布,并分析了影响路桥隧施工的常见设计因素,可供施工技术管理参考.

Methods of Random Fatigue Design for Vehicle Transmission System Components

Some representative working conditions were measured, and the amplitude distribution rule of each representative working condition after analysis of measured data was got. The building of 2 D distributing function between the range and the mean of random load was discussed. Experiment was carried out to get the fatigue strength data of the material of transmission component. Accessing the P S a S m N camber of combined load of bending and torsion on this material after analysis. And the process of calculating the 2 D fatigue life in multi working condition was discussed.

A New Probability Distribution Function of Wave Steepness

Wave steepness is an important characteristic describing the severity of sea state in ocean engineering. In the existing theoretical and experimental studies,wave steepness is often substituted by some related quantities. In this paper,a new probability density function(pdf) of steepness,which is a pdf of the steepness in its original definition,is obtained for narrowband Gaussian processes. The drawback inherent in the previous theoretical pdfs of steepness,that is,the probability density at zero steepness is nonzero,has been eliminated. Laboratory experiments were conducted in a wind-wave flume to measure the wave steepness distribution. Comparisons among laboratory measurements and some theoretical pdfs of steepness show that the new pdf generally fits the data better than the one proposed by Zheng et al.(1999) .

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

Distributed Consensus-Based K-Means Algorithm in Switching Multi-Agent Networks

This paper discusses a distributed design for clustering based on the K-means algorithm in a switching multi-agent network, for the case when data are decentralized stored and unavailable to all agents. The authors propose a consensus-based algorithm in distributed case, that is, the double- clock consensus-based K-means algorithm （DCKA）. With mild connectivity conditions, the authors show convergence of DCKA to guarantee a distributed solution to the clustering problem, even though the network topology is time-varying. Moreover, the authors provide experimental results on vari- ous clustering datasets to illustrate the effectiveness of the fully distributed algorithm DCKA, whose performance may be better than that of the centralized K-means algorithm.

Bayesian Empirical Likelihood Estimation of Quantile Structural Equation Models

Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and explanatory latent variables follow the normal distribution, and the effect of explanatory latent variables on outcomes can be formulated by a mean regression-type structural equation. But this SEM may be inappropriate in some cases where random errors or latent variables are highly nonnormal. The authors develop a new SEM, called as quantile SEM(QSEM), by allowing for a quantile regression-type structural equation and without distribution assumption of random errors and latent variables. A Bayesian empirical likelihood(BEL) method is developed to simultaneously estimate parameters and latent variables based on the estimating equation method. A hybrid algorithm combining the Gibbs sampler and Metropolis-Hastings algorithm is presented to sample observations required for statistical inference. Latent variables are imputed by the estimated density function and the linear interpolation method. A simulation study and an example are presented to investigate the performance of the proposed methodologies.