SRF in collaboration with the Agricultural Utilization Research Institute (AURI) recently evaluated the cost-effectiveness of RePlay, a bio-based asphalt pavement preservation agent. The potential for cost savings is an important driver for new asphalt pavement preservation techniques. RePlay is one of the few bio-based products recently invented and introduced as a fog sealant for pavement preservation purposes. The product contains approximately 15% polymers, which increases pavement resistance to raveling, rutting, and cracking (Figure 1). RePlay has been used in several locations across Minnesota.
Figure 1. Untreated Pavement, with RePlay applied, and then Replay after treatment (Credit: BioSpan)
SRF conducted statistical analysis (including use of machine learning methods) using pavement performance data from the City of Hutchinson, Minnesota, which indicated that RePlay slows the rate of degradation of asphalt (Figure 2). We applied service-life estimation methods and performed a life-cycle cost analysis (LCCA) in conjunction with a sensitivity analysis. Service-life prediction results revealed that the application of RePlay may increase the longevity of an untreated roadway surface up to seven years (Figure 3 and 4).
The results show RePlay to be a financially viable surface treatment that public agencies can use to reduce the maintenance cost of low-volume roads (Figure 5). Recommendations that may result from this research may also provide public transportation agencies with an added level of confidence in predicting the financial results of pavement treatment alternatives. In addition, another major contribution of this study is service life estimation of a newly developed pavement preservation method using machine learning, which can be used in pavement and asset management studies.
SRF’s Ali Nahvi and Jacqueline Nowak developed a data-driven economic analysis approach to help AURI estimate service life and life-cycle cost of RePlay compared with untreated asphalt concrete. Data engineering practices were used to collect and clean RePlay performance measure data and prepare a dataset for additional analysis. Then, a statistical analysis was conducted to compare the deterioration rate of RePlay with untreated pavement. In the next step, a machine learning model was used to estimate service life of a pavement surface treated with RePlay. At the end, SRF compared life-cycle cost of RePlay with an untreated asphalt concrete using economic analysis methods.
Figure 2. Plot of PCI Values Over Time: Untreated Section vs. RePlay Section
Figure 3. Three Possible RePlay Implementation Scenarios
Figure 4. Service Life Prediction Model for RePlay Scenarios
Figure 5. Outcome of LCCA – RePlay vs. Untreated