14 research outputs found
Processing of selected terminology at road accident analysis
Get PDFPráce se zabývá problematikou terminologie užívané v soudně-inženýrské praxi při analýze silničních nehod (ASN). Jedná se o definování pojmů popisujících specifické znalecké zkoumání v oblasti ASN jakými jsou možnost odvrácení střetu a možnost zabránění střetu. Dále se práce zaměřuje na terminologii užívanou v oblastech úzce spojených s ASN jako je řešení případů páchání trestné činnosti v oboru pojištění motorových vozidel z pohledu technického znalce. V této oblasti jsou řešené pojmy rozsah poškození, charakter poškození, korespondence poškození a technická přijatelnost nehodového děje vzájemně propojeny v určitý metodický postup, který může posloužit, jako pomůcka pro znalce při řešení takových případů. Zpracovány jsou i vybrané pojmy z technicko-právní oblasti, se kterými se znalci často setkávají, přitom neexistuje jednotný přístup pro jejich hodnocení z technického pohledu. Zde se jedná o možnosti stanovení hranice náhlé změny rychlosti jízdy a náhlé změny směru jízdy vozidla.The thesis deals with the terminology used in forensic engineering practice at the analysis of road accidents. It means defining the concepts describing specific expert research in the area of car accident analysis such as the option of evasive action and the possibility of preventing collision. Further on, the thesis focuses on the terminology used in areas closely related to analysis of road accidents namely dealing with cases of criminal activities in the field of motor vehicle insurance from an expert point of view. In this area, terms such as extent of the damage, character of the damage, correspondence of the damage and the technical acceptability of accident, are solved. These terms constitute a formal process that can serve as a tool for experts when dealing with such cases. In addition, selected terms from the technical-legal area, which the experts often encounter, are processed here. At the same time, there is no uniform approach to their evaluation from a technical perspective. Here, there is a possibility of setting the limits of a sudden change of speed and a sudden change of direction.
Safe or unsafe driving maneuvers in road traffic
Get PDFThis article represents an area closely connected to the analysis of road accidents. When the act of accident is being considered by an expert, it often happens that one participant of the accident causes that the other participant has to change suddenly the speed or direction of his/her drive to prevent possible collision. For a proper consideration of the driving manoeuvres we need to know the limit of a sudden change of speed or a sudden change of direction of drive. The limit of suddenness can be taken as the limit between a safe and already unsafe driving manoeuvre (braking, transverse motion) of the vehicle. The author tried to settle this limit by measurements with respondents who were asked to subjectively classify particular driving manoeuvres. For classification of the sudden change of speed, the intensity of deceleration was altered (3 m/s2, 5 m/s2, 7 m/s2, and maximum attainable deceleration amax m/s2). At classifying a sudden change of direction, we changed the trajectory of the vehicle´s motion with transverse motion in one curve. As a result in the change of trajectory, the magnitude of side acceleration changed (values of side acceleration 2 m/s2, 3 m/s2, 4 m/s2 and 6 m/s2). From the results achieved it can be settled that the limit of a sudden change of vehicle´s speed went round a half of maximally achievable braking deceleration of personal vehicles, at approximately 3,8 to 4,8 m/s2. The limit of a sudden change of direction of drive went between 2,7 and 3,5 m/s2.of vehicles´ side acceleration. Achieved knowledge can be used in the sphere of road accident analysis when classifying driving manoeuvres of individual participants of the scene of accident. All measurements were taken with personal vehicles, thus it is possible to pay further attention to setting the limit of a sudden change of speed and direction of drive with utility vehicles, lorries and trucks, respectively
The Hilltop 2-19-1982
Get PDFThis document created through a generous donation of Mr. Paul Cottonhttps://dh.howard.edu/hilltop_198090/1043/thumbnail.jp
Adolescence and Cardiac Channelopathies: Predicting Engagement in High-Risk Behavior
Get PDFThe purpose of the current study was to explore the type and frequency of engagement in low to high-risk behaviors in adolescents with cardiac channelopathies. Additionally, predictive factors of engagement in developmentally typical behaviors were explored. Individuals ages 13-22 (female = 84.6%; average age = 19.53) with cardiac channelopathies (n = 10) and without any chronic health conditions (n = 4) completed a series of questionnaires examining self-reported quality of life, illness perceptions, problem solving, and risk-taking behaviors. Findings demonstrated that adolescents with cardiac conditions believe that their conditions are controllable, and they experience a moderate amount of medically-related symptoms. Adolescents with and without cardiac channelopathies self-reported similar levels of quality of life. Adolescents with cardiac channelopathies had significantly better overall social problem solving skills than the control condition; in addition, they had better problem identification, solution evaluation, and reorganization skills than participants without cardiac channelopathies. Whether certain factors are predictive of engagement in risk-taking behaviors was unable to be examined, due to small sample size. There were no differences in risk-taking behaviors between participants with and those without cardiac channelopathies. However, participants with cardiac channelopathies self-reported engagement in behaviors that can impact their health. Future studies should continue to examine risk-taking within this medical population and identify variables that are predictive of risk-taking
Automated Productivity Models for Earthmoving Operations
Get PDFEarthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity.
This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects.
The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions.
Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively
