我国船舶溢油污染事故溢油量评估方法及其应用

鉴于污染面积和溢油量对溢油污染事故的重要性,本文作者根据多年工作经验,从实际应用出发,给出了适用于我国船舶溢油污染事故污染面积和溢油量评估的几种技术方法,并给出了应用实例.同时阐述了每种评估方法的实质以及其自身的技术局限性,以及在实例中发挥的作用.旨在推进我国的污染面积和溢油量评估工作,实现规范化、系统化评估,提高船舶溢油污染事故的处理效率.     

一起氯碱厂化学品泄漏事故原因浅析

通过对一起储罐破裂导致的化学品泄漏事故原因分析,说明了次氯酸钠与盐酸是禁忌物质,建议设计、评价人员应全面深刻理解国家有关安全标准和设计规范,正确设计;生产使用单位接受事故教训,防止同类事故重演。     

GIS技术在化学事故应急指挥系统中的应用研究

以地理信息系统（简称GIS）的技术特点为基础,结合应急指挥系统中的功能需求,探讨化学事故应急指挥系统GIS平台能够实现的功能,以及为实现这些功能而构建的数据库的建设。将GIS技术应用在化学事故应急指挥系统中,能够迅速、直观地了解事故装置和设备情况,获得有关信息,进行事故模拟。对提高事故处理速度,缩短反应时间,减少事故损失具有重要意义。     

软岩隧道塌方事故致灾因素耦合分析*

为减少专家主观判断对软岩隧道塌方事故评估的影响,提出1种从事故出发逆推分析事故致灾因素耦合机制的方法。基于142个隧道施工塌方事故案例,系统总结隧道塌方事故致灾因素,对塌方事故的致灾因素出现频率进行排序;从致灾因素之间关联耦合关系出发,结合隧道施工塌方事故的多因素耦合致灾机理,研究隧道塌方事故产生过程的多因素耦合路径和耦合过程;采用N-K耦合模型开展隧道塌方事故多因素耦合路径下的耦合关联值评估,并对耦合关联值进行排序从而找到控制塌方事故发生的致灾因素组合。结果表明:除4个主要致灾因素的全耦合外,围岩岩性-降雨-地下水的耦合关联值是洞身段中最大值,为17.79%,围岩岩性-偏压-地下水的耦合关联值是洞口段中最大值,为24.02%;洞身段的围岩岩性-地下水因素和洞口段的围岩岩性-偏压因素分别对耦合关联值大小起决定影响;耦合关联值不具备叠加效应,2因素耦合关联值可能比3因素耦合关联值更大。研究结果可为提高隧道事故分析与安全防控提供科学依据。     

建筑工程生产事故死亡人数时间序列分析

为研究建筑工程安全生产事故死亡人数的变化规律,采用时间序列分析方法,分析了建筑安全事故死亡人数时间序列上的趋势性规律,通过数据预处理和模型的识别与检验,最终建立了安全事故死亡人数预测模型。对全国2005—2014年建筑工程安全生产事故造成的死亡人数进行了分析和预测。结果表明:ARIMA模型各年预测值与实际值误差率为0.393,相比灰色模型和BP神经网络模型误差率最小。总体上说,ARIMA模型较适用于随机性较大的数据的趋势预测。     

行为实验视角下煤矿员工不安全行为路径分析

面对煤矿事故频发现状,立足矿井安全实际生产困难,深入探索矿工不安全行为影响因素。研究表明,煤矿安全事故中,员工不安全行为是导致事故发生的主要原因。通过建立实验平台模拟煤矿员工工作现场,比较不同文化程度的矿工不安全行为发生率以及安全文化教育后矿工不安全行为发生率趋势。对比发现,文化程度越高的员工的不安全行为发生次数不一定越低,且安全文化教育的刺激作用具有保质期。     

Analysis of occupational accidents: prevention through the use of additional technical safety measures for machinery

This article presents an analysis of results of 1035 serious and 341 minor accidents recorded by Poland's National Labour Inspectorate (PIP) in 2005–2011, in view of their prevention by means of additional safety measures applied by machinery users. Since the analysis aimed at formulating principles for the application of technical safety measures, the analysed accidents should bear additional attributes: the type of machine operation, technical safety measures and the type of events causing injuries. The analysis proved that the executed tasks and injury-causing events were closely connected and there was a relation between casualty events and technical safety measures. In the case of tasks consisting of manual feeding and collecting materials, the injuries usually occur because of the rotating motion of tools or crushing due to a closing motion. Numerous accidents also happened in the course of supporting actions, like removing pollutants, correcting material position, cleaning, etc.     

Multibody model of the human upper extremity for fracture simulation

About 3.8 million people are injured in accidents at work in Europe every year. The resulting high costs are incurred by the victims themselves, their families, employers and society. We have used a numerical simulation to reconstruct accidents at work for several years. To reconstruct these accidents MADYMO R7.5 with a numerical human model (pedestrian model) is used. However, this model is dedicated to the analysis of car-to-pedestrian accidents and thus cannot be fully used for reconstructing accidents at work. Therefore, we started working on the development of a numerical model of the human body for the purpose of simulating accidents at work. Developing a new numerical model which gives an opportunity to simulate fractures of the upper extremity bones is a stage of that work.     

Analysis of car-to-bicycle approach patterns for developing active safety devices

Objective: To reduce the severity of injuries and the number of cyclist deaths in traffic accidents, active safety devices providing cyclist detection are considered to be effective countermeasures. The features of car-to-bicycle collisions need to be known in detail to develop such safety devices.

Methods: The study investigated near-miss situations captured by drive recorders installed in passenger cars. Because similarities in the approach patterns between near-miss incidents and real-world fatal cyclist accidents in Japan were confirmed, we analyzed the 229 near-miss incident data via video capturing bicycles crossing the road in front of forward-moving cars. Using a video frame captured by a drive recorder, the time to collision (TTC) was calculated from the car's velocity and the distance between the car and bicycle at the moment when the bicycle initially appeared.

Results: The average TTC in the cases where bicycles emerged from behind obstructions was shorter than that in the cases where drivers had unobstructed views of the bicycles. In comparing the TTC of car-to-bicycle near-miss incidents to the previously obtained results of car-to-pedestrian near-miss incidents, it was determined that the average TTC in car-to-bicycle near-miss incidents was significantly longer than that in car-to-pedestrian near-miss incidents.

Conclusions: When considering the TTC in the test protocol of evaluation for safety performance of active safety devices, we propose individual TTCs for evaluation of cyclist and pedestrian detections, respectively. In the test protocols, the following 2 scenarios should be employed: bicycle emerging from behind an unobstructed view and bicycle emerging from behind obstructions.     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.