United States Department of Transportation
i
Effects of Bridge Surface & Pavement Maintenance Activities on Asset Rating
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2017-11-01
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[PDF-5.14 MB]
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Alternative Title:Effects of Bridge Surface and Pavement Maintenance Activities on Asset Rating
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Abstract:Treatment effectiveness modeling is essential for asset performance modeling and predictions, and ultimately, for effective performance monitoring and feedback and for evaluating and comparing alternative treatments. Asset managers use these models to determine the expected incremental change of asset condition resulting from a future application of a specific maintenance treatment. That way, the agency can update its asset performance curves in software simulation to reflect maintenance application at any future year and to identify the most cost-effective treatments. Asset managers also seek to identify the factors that influence treatment effectiveness, and to use cost and performance data to estimate the cost-effectiveness of these treatments. This study provides and demonstrates a methodology to quantify the impact of INDOT’s standard maintenance treatments on state highway pavements and bridge deck surfaces, in terms of their condition ratings. Of the specific objectives, the first is to generate requisite reset values that INDOT’s asset manager can use in the agency’s PMS and BMS software packages. The second is to measure the longer-term effectiveness of specific maintenance treatments in terms of the extension to asset life. The third specific objective is to use this information to assess the cost-effectiveness of the treatments. The research product from this project is a set of averages or models that represent the impacts (performance jump, post-treatment performance vs. age relationship, and cost) of each treatment type typically applied to INDOT’s assets. The performance impacts are expressed in terms of the requisite performance indicators. The performance jump models showed that the asset’s functional class and pre-treatment condition, and the treatment type were major significant predictors of the performance jump and post-treatment performance loss. The first deliverable from this project is the average (mean) impact for each treatment type under investigation. The second is the overall statistical description of the impact, namely, the minimum and maximum impact, and range and standard deviation of impact; a statistical model that predicts the impact as a function of asset and treatment attributes. The third is a set of charts that describe the sensitivity of the treatment impact to factors related to the asset or the treatment. The study also developed cost models for each of the pavement and bridge treatments and used these results to assess the long-term cost-effectiveness of the treatments.
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