Modeling asset deterioration is a key business process within Transportation Asset Management. Road agencies should budget a large amount of public money to reduce the number of accidents and achieve a high level of service of the road system. Managing and preserving those investments is crucial, even more in the actual panorama of limiting funding. Therefore, roadway agencies have to increase their efforts on monitoring pavement networks and implementing data processing tools to promote cost-effective Pavement Management System (PMS) strategies. A comprehensive PMS database, in fact, ensures reliable decisions based on survey data and sets rules and procedures to analyze data systematically. However, the development of adequate pavement deterioration prediction models has proven to be difficult, because of the high variability and uncertainty in data collection and interpretation, and because of the large quantity of data information from a wide variety of sources to be processed. This research proposes a comprehensive methodology to design and implement pavement management strategies at the network level, based on road agency local conditions. Such methodology includes the identification of suitable indexes for the pavement condition assessment, the design of strategies to collect pavement data for the agency maintenance systems, the development of data quality and data cleansing criteria to support data processing and, at last, the implementation spatial location procedures to integrate pavement data involved in the comprehensive PMS. This work develops network-level pavement deterioration models, and reviews road agency preservation policies, to evaluate the effectiveness of maintenance treatment, which is essential for a cost-effective PMS. It is expected that the resulting methodology and the developed applications, product of this research, will constitute a reliable tool to support agencies in their effort to implement their PMS.
Identification of cost-effective pavement management systems strategies a reliable tool to enhance pavement management implementations
PANTUSO ROMERO, ANTONIO
2019
Abstract
Modeling asset deterioration is a key business process within Transportation Asset Management. Road agencies should budget a large amount of public money to reduce the number of accidents and achieve a high level of service of the road system. Managing and preserving those investments is crucial, even more in the actual panorama of limiting funding. Therefore, roadway agencies have to increase their efforts on monitoring pavement networks and implementing data processing tools to promote cost-effective Pavement Management System (PMS) strategies. A comprehensive PMS database, in fact, ensures reliable decisions based on survey data and sets rules and procedures to analyze data systematically. However, the development of adequate pavement deterioration prediction models has proven to be difficult, because of the high variability and uncertainty in data collection and interpretation, and because of the large quantity of data information from a wide variety of sources to be processed. This research proposes a comprehensive methodology to design and implement pavement management strategies at the network level, based on road agency local conditions. Such methodology includes the identification of suitable indexes for the pavement condition assessment, the design of strategies to collect pavement data for the agency maintenance systems, the development of data quality and data cleansing criteria to support data processing and, at last, the implementation spatial location procedures to integrate pavement data involved in the comprehensive PMS. This work develops network-level pavement deterioration models, and reviews road agency preservation policies, to evaluate the effectiveness of maintenance treatment, which is essential for a cost-effective PMS. It is expected that the resulting methodology and the developed applications, product of this research, will constitute a reliable tool to support agencies in their effort to implement their PMS.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/98312
URN:NBN:IT:UNIROMA1-98312