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Construction inspection is a critical component of INDOT’s quality assurance (QA) program. Upon receiving an inspection notice/assignment, INDOT inspectors review the plans and specifications to identify the construction quality requirements and conduct their inspections accordingly. This manual approach to gathering inspection requirements from textual documents is time-consuming, subjective, and error-prone. This project addresses this critical issue by developing an inspection requirements database along with a set of tools to automatically gather the inspection requirements and provide field crews with customized construction checklists during the inspection with the specifics of what to check, when to check, and how to check, as well as the risks and the actions to take when noncompliance is encountered. This newly developed toolset eliminates the manual effort required to acquire construction requirements, which will enhance the efficiency of the construction inspection process at INDOT. It also enables the incorporation of field-collected data to automate future compliance checking and facilitate construction documentation.

Acceptance testing is a critical aspect of the quality control and quality assurance (QC/QA) program to ensure the reliable long-term performance of pavement. A typical acceptance testing specification includes acceptable quality characteristics (AQCs), testing methods, number of samples, sample locations, and acceptance criteria. In the current practice, Indiana Department of Transportation (INDOT) accepts pavement by sampling and testing materials with a pre-determined, very low frequency at random locations, leading to a significant problem: testing results are not truly “representative” of the project because sampling is neither based on a statistical foundation, nor on the reliability concept. This study developed a systematic guideline that has addressed the aforementioned problem of material acceptance testing in four aspects: identifying key material properties for testing, selecting sample locations, designing acceptance criteria, and determining optimal sample size. Key material properties that are critical to the pavement long-term performance are identified by comparing with sensitive material properties in MEPDG. A random sampling mechanism was devised based on two spatial indices to control the spatial pattern of samples to minimize the influence from spatial autocorrelation. Risk-based acceptance criteria was proposed based on statistical methods to control the agency’s risk at a desired level given a specific sampling and testing strategy, based on which optimal sample size is determined from a risk perspective. Cost analysis approaches were developed to estimate the total cost of acceptance testing by integrating the risk of making incorrect decision and enable the determination of optimal sample size from a cost perspective. Additionally, quality control chart was exploited as a complementary tool to ensure the consistency of the pavement quality of a project. The results of this study were validated using real data from INDOT projects, and a web t

Construction inspection is a critical component in the quality assurance (QA) program to ensure the quality and long-term performance of pavements. Over the years, INDOT has been developing and modifying its standard specification to set requirements for construction inspection and material testing. With the retirement of experienced employees, INDOT is challenged with the lack of knowledge to effectively inspect the critical elements of construction results/deliverables such as pavement, soil embankment, and bridge (decks). There is a critical need for INDOT to allocate limited resources to the riskiest areas and equip construction inspectors with necessary knowledge to conduct inspection, ensure the quality of construction results, and minimize risks to INDOT. This study developed a risk-based inspection guide that has addressed the aforementioned problems of shortage in staffing and loss and lack of knowledge by providing answers in aspects of what, when, how, and how often to inspect. A comprehensive list of testing and inspection activities were extracted from INDOT’s material testing manual, INDOT’s standard specification, and the QA implementation at the Ohio River Bridge (ORB) project. This list was narrowed down to a core set of items based on survey responses and interviews with INDOT domain experts. Testing and inspection activities in the core set were aligned with the construction process. The risk associated with each inspection activity was assessed by considering both the probability of failure and consequence severity of failure in four dimensions: cost, time, quality, and safety. A composite risk index was developed as a single measure for the overall risk. All inspection activities were prioritized based on the composite index. For implementation, a linking mechanism was developed to link inspection activity, pay item, and check items (extracted from specification). This linking mechanism aligns with the business process of construction inspection

This report describes a study of intelligent compaction (IC) technologies, within the context of actual construction projects, for its potential as a component of INDOT’s QC/QA for soils. The output from an IC-equipped roller compaction equipment is a real-time area mapping of the compacted lift stiffness as captured by the IC measure. Data was collected to evaluate the correlation between each of two IC measures—compaction meter value (CMV) and machine drive power (MDP)—and in situ embankment quality test measures, the chief in situ test being the dynamic cone penetrometer (DCP) test which INDOT uses for soil embankment acceptance testing. Researchers sought to understand how well the IC measures might assess embankment quality as currently evaluated by the in situ measures. Window-averaged IC measures were compared with the in situ DCP test points. For CMV, a variable correlation was found between the average CMV and DCP values from 74 in situ locations. Also, a limited head-to-head comparison of CMV and MDP with the in situ measures provided some indication that MDP should be studied further. Lessons were learned regarding the elimination of bias in future correlation studies, critical provisions to facilitate best data quality, and important aspects of data management. IC technology holds promise for monitoring the consistency of the soil compaction effort and flagging weak areas in real time during compaction operations. However, further insight is needed regarding the correlation of the DCP measure with both types of IC measures for various soil characterizations and field moisture conditions.

This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT’s pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a “lining-up” methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data from Indiana’s highway network. The results showed that the errors in the unlined up data are significant as they mischaracterize the true pavement condition. This could lead to the reporting of unreliable information of road network condition to the decision makers, ultimately leading to inappropriate condition assessments and prescriptions. Benefits of the methodology reverberate throughout the management functions and processes associated with highway pavements in Indiana, including pavement performance modeling, optimal timing of maintenance, rehabilitation, and reconstruction (MRR), and assessment of the effectiveness of MRR treatments and schedules. The second part of the study developed correlations for the different types of pavement distresses using machine learning algorithms. That way, the severity of any one type of distress can be estimated based on known severity of other distresses at that location. The 2012-2014 data were from I-70, US-41, and US-52, and the distress types considered are cracking, rutting, faulting, and roughness. Models were developed to relate surface roughness (IRI) to pavement cracks, and between the different crack types, with resulting degrees of confidence that varied across the different crack types and road functional classes. In addition, for eac

State Transportation Agencies (STAs) use quality control/quality assurance (QC/QA) specifications to guide the testing and inspection of road pavement construction. Although failed materials of pavement rarely occur in practice, it is critical to have a sound decision framework to assist in making data-driven, informed decisions regarding failed materials because such decisions have profound impacts on the long-term performance of the pavement and the operation and maintenance costs of the responsible highway agencies. A performance-related specification (PRS) is a quality acceptance (QA) specification that specifies the acceptable levels of key acceptance quality characteristics (AQCs) that are directly related to fundamental engineering properties, which in turn, determine the long-term performance of the constructed end products. Two PRS tools, PaveSpec for Portland Cement Concrete Pavement (PCCP) and Quality Related Specification Software (QRSS) for QC/QA Hot Mixed Asphalt (HMA) pavement, were investigated in this study to develop decision frameworks for PCCP and HMA pavement to assist the decision-making regarding failed materials at INDOT. A large number of simulations of various scenarios in the context of INDOT pavement construction were conducted to fully develop and implement the decision framework. For PCCP, the newly developed decision framework based on PaveSpec was validated using data from an INDOT construction project. The framework is readily implementable to assist INDOT in making informed decision regarding failed materials for PCCP. For QC/QA pavement, it was found that QRSS is not an appropriate PRS tool to estimate the long-term performance because of its limitations, the misalignment between QRSS process and INDOT practice, and erroneous simulation results.