CHAPTER 1

Introduction

Background

3D laser-based pavement imaging systems have been widely adopted by departments of transportation (DOTs) to conduct automated pavement condition assessments. 2D imaging technologies and smartphones are also being used to conduct pavement condition evaluations, especially by local transportation agencies. Collected pavement images are used to identify pavement distresses using various semi- and fully automated methods. Among these methods, models based on Artificial Intelligence (AI) with machine learning/deep learning (ML/DL) have gained attention in the last several years.

The collected functional and structural distresses are key indicators of triggering pavement maintenance and rehabilitation activities. If developers do not clearly understand DOTs’ use of distress data, AI model development efforts for distress detection and/or classification, including AI model formulation, distress annotation, training, and performance evaluation, can be misguided and fail to reach their full potential. For example, automated distress detection methods based on the use of image blocks to classify distress may not be able to output accurate information on distress extent and severity. Therefore, the outcome generated by the model may not meet an agency’s needs for project-level applications, such as planning maintenance and rehabilitation activities.

The performance of supervised-learning AI models for automated pavement distress extraction relies heavily on several factors, including data collection, formatting, quality (i.e., resolution), pavement image size, annotation quality (labeled ground truth distresses), and model formulation and training. However, the performance evaluation method used for developed models is not always clear, especially as it pertains to the diversity of the data used for evaluation and ground truth. The lack of consistency in clarity makes the performance comparison of different AI models challenging.

Objective

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

Scope and Approach

This study focused on DOT automated pavement condition survey (APCS) practices and their use of AI to assess pavement surface distress. Information collected included:

• Requirements for automated pavement distress identification,
• Applications of pavement distress condition information,

• Types of agency decision-making supported by pavement condition data, and
• Uses of AI (e.g., ML/DL) technologies for pavement distress detection and classification.

Methods for collecting the desired information included a literature review, a DOT questionnaire, and follow-up questions as needed. The literature search results were used to develop a survey of DOT practices, which was distributed to each state DOT, the District of Columbia DOT (District DOT), and the Puerto Rico Highway and Transportation Authority.

To obtain more detailed information related to practice, follow-up questions were sent to agencies that indicated using AI technologies for automated pavement condition distress identification. The follow-up questions investigated data collection requirements, AI processing efforts, and the ability to assess historical data.

The information obtained from the literature review, the survey of practice, and the follow-up questions provided the basis for this synthesis.

Report Organization

This synthesis is organized into the following chapters:

1. Chapter One—Introduction. This chapter provides background information and synthesis objectives and summarizes the scope, approach, and organization of the document.
2. Chapter Two—Literature Review. This chapter summarizes the findings of the literature review. Relevant topics covered in the literature review include automated cracking measurement protocols, AI data acquisition, models, factors that affect models, and data acceptance.
3. Chapter Three—State of the Practice. This chapter summarizes the DOT survey results and includes topics related to DOT practices on APCSs, the use of AI technology, data and image requirements, distress quantification, the use of survey results, and the benefits and challenges of using AI technologies.
4. Chapter Four—Case Examples. This chapter summarizes two examples, one illustrating the manual review of images for the quality control of AI results and the other illustrating the evaluation of an AI algorithm. The relatively recent developments and implementation of AI and associated 3D technology for APCS analysis precluded the development of DOT-specific case examples providing details and specifics at present.
5. Chapter Five—Conclusions. The synthesis concludes with a summary of key findings and suggested areas for future research and outreach to further utilize AI technologies.
6. Appendices—Appendix A presents the DOT questionnaire, and Appendix B summarizes the DOTs’ responses to each survey question.