Contracting for Pavement Distress Data Collection

1998 ◽  
Vol 1643 (1) ◽  
pp. 80-85 ◽  
Author(s):  
Roger E. Smith ◽  
Thomas J. Freeman ◽  
Olga J. Pendleton
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Data Collection ◽  
Pavement Management ◽  
Management Systems ◽  
Automated Data Collection ◽  
Pavement Distress ◽  
Data Collection Methods ◽  
Pavement Management Systems ◽  
Collection Methods

Many agencies responsible for managing pavements have adopted pavement management systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS is collecting condition information, especially pavement distress information. Many agencies have started to contract for pavement distress data collection. Some of the agencies have experienced problems with the data collected by contract. A study for agencies in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described. Issues about quality control and quality assurance faced by agencies considering contracting for automated data collection also are raised. These issues need additional study to develop appropriate guidelines. The initial set provided is based on discussions with some of the agencies currently contracting for pavement distress data collection.

scholarly journals Preanalytical errors in medical laboratories: a review of the available methodologies of data collection and analysis

2016 ◽  
Vol 54 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Jamie West ◽  
Jennifer Atherton ◽  
Seán J Costelloe ◽  
Ghazaleh Pourmahram ◽  
Adam Stretton ◽  
...  
Keyword(s):  
Patient Safety ◽  
Quality Management ◽  
Data Collection ◽  
Quality Indicators ◽  
Management Systems ◽  
Continual Improvement ◽  
Laboratory Errors ◽  
Data Collection Methods ◽  
The Impact ◽  
Collection Methods

Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture.

scholarly journals Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 319 ◽  
Author(s):  
Ronald Roberts ◽  
Gaspare Giancontieri ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Road Networks ◽  
Integrated Approach ◽  
Pavement Management ◽  
Management Systems ◽  
Hotspot Analysis ◽  
Pavement Distress ◽  
Industry Standards ◽  
Pavement Management Systems

Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

scholarly journals KINECT, A NOVEL CUTTING EDGE TOOL IN PAVEMENT DATA COLLECTION

2015 ◽  
Vol XL-1-W5 ◽  
pp. 425-431 ◽  
Author(s):  
A. Mahmoudzadeh ◽  
S. Firoozi Yeganeh ◽  
A. Golroo
Keyword(s):  
Data Collection ◽  
User Satisfaction ◽  
Capital Investment ◽  
Cost Effective ◽  
Pavement Management ◽  
Automated Data Collection ◽  
Pavement Management Systems ◽  
Pavement Roughness ◽  
Rgb Images ◽  
Surface Distress

Pavement roughness and surface distress detection is of interest of decision makers due to vehicle safety, user satisfaction, and cost saving. Data collection, as a core of pavement management systems, is required for these detections. There are two major types of data collection: traditional/manual data collection and automated/semi-automated data collection. <br><br> This paper study different non-destructive tools in detecting cracks and potholes. For this purpose, automated data collection tools, which have been utilized recently are discussed and their applications are criticized. The main issue is the significant amount of money as a capital investment needed to buy the vehicle. <br><br> The main scope of this paper is to study the approach and related tools that not only are cost-effective but also precise and accurate. The new sensor called Kinect has all of these specifications. It can capture both RGB images and depth which are of significant use in measuring cracks and potholes. This sensor is able to take image of surfaces with adequate resolution to detect cracks along with measurement of distance between sensor and obstacles in front of it which results in depth of defects. <br><br> This technology has been very recently studied by few researchers in different fields of studies such as project management, biomedical engineering, etc. Pavement management has not paid enough attention to use of Kinect in monitoring and detecting distresses. This paper is aimed at providing a thorough literature review on usage of Kinect in pavement management and finally proposing the best approach which is cost-effective and precise.

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