CHAPTER 5

Conclusions

The progression of pavement condition surveys from manual to fully automated methods has provided opportunities to use AI technologies for a more efficient and accurate detection of pavement surface distresses (e.g., cracking, rutting, potholing). As with conventional pavement condition surveys, AI-based analysis requires model development (or selection), training, and validation.

The objective of this synthesis is to document state DOT practices of automated pavement distress identification and AI (i.e., ML/DL) technologies for pavement condition evaluation.

The synthesis is based on the results of a literature review and a survey of state DOTs, the District DOT, and the Puerto Rico Highway and Transportation Authority. As discussed in Chapter 4, no case examples could be developed due to a lack of DOTs with the necessary experience with AI technology.

Overall Findings

Several AI modeling techniques are currently available and used for APCS analysis; these models include DL (e.g., classification, object detection, and semantic segmentation), ML (e.g., SVM, DT, RF, boosting, and ANN), and image processing (e.g., edge detection, threshold, and manual filters) methods. While AI technologies improve the efficiency and accuracy of APCS analysis, acceptance of AI results requires ground truth testing and model validation.

In total, 43 of the 52 surveyed DOTs (83%) responded to the survey, which asked questions about APCS, AI technology, data and image requirements, distress types evaluated, the use of APCS results, and the challenges and benefits of using AI. The majority of DOTs (38 of 43, 88%) indicated using an APCS to quantify pavement conditions. However, only 8 of 38 DOTs (21%) indicated that AI technology was used to analyze the APCS. Predominant distress types evaluated using AI technology included transverse and longitudinal cracking (7 of 8 DOTs, 88%, for both) and alligator cracking (5 of 8 DOTs, 63%) for asphalt pavements and transverse and longitudinal cracking (6 and 5 of 7 DOTs, 86% and 71%, respectively) for concrete pavements.

AI technology provided several benefits, including objectivity (14 of 25 DOTs, 56%), accuracy and increased productivity (13 of 25 DOTs, 52%), and cost savings (7 of 25 DOTs, 28%). Conversely, some of the challenges DOTs faced with AI technology included accuracy, precision, and repeatability (6 of 24 DOTs, 25%); comparison to manual surveys (3 of 24 DOTs, 13%); use of random and predefined reference sections (3 of 24 DOTs, 13%); and comparison to traditional APCS (2 of 24 DOTs, 8%).

Suggestions for Future Research

Suggested future research for improving the use of AI technology in analyzing APCS results includes the following:

• Images for AI Technology. Having a standard image format, data set, and distress library would significantly benefit agencies in developing, calibrating, training, and validating AI models. Develop a standard image format (e.g., .jpeg, .png, or a private extension) and a publicly available distress data set (including, pavement type, distress type, and severity level) to assess APCS results using AI technology.
• Tool for Distress Library. Develop a software tool, using traditional computer vision techniques, to assist agencies in building the AI distress library.
• Guidance for Using AI Models. Prepare a guidance document to assist agencies in using and implementing AI technology in an APCS. The guidance document is intended to provide detailed information related to AI technology (including ML/DL); selection, development, or modification of existing AI models; model training; calibration and recalibration; and approaches for validation, quality control, and acceptance.
• AI Analysis of Historical Data. Agencies have been conducting APCS for decades; however, technology and data collection protocols have changed over this same time. Develop a methodology to use AI models to interpret APCS results from older technology and compare data collection and analysis protocols.
• APCS Data Collection Framework. APCS can generate large volumes of data, especially for large roadway networks. In addition, storage and management of numerous years of data can be challenging. Develop a framework for data collection, storage, and management.
• AI Training Course. AI technology is being used by APCS vendors; however, how AI is used for distress detection is not clearly understood. Develop a training course to provide information and educate participants on how AI technology is used to identify pavement distress.
• Update Current Distress Protocols. With the adoption of AI technologies, the current distress protocols may require revisions. As part of this research, review current distress protocols, identify gaps, and propose revisions to address the use of AI for distress detection.