Indiana Department Of Transportation Research Articles

Reducing Distracted Driving and Improving Consistency with Brine Truck Automation

Salt brine is routinely used by transportation agencies to pre-treat critical infrastructure such as bridges, ramps, and underpasses in advance of winter storms. This requires an operator turning on and off brine controls while driving at highway speeds, introducing driver distraction and consistency challenges. In urban areas, such as Indianapolis, a 5500-gallon tractor trailer with a gross vehicle weight of 80,000 pounds is typically used and the driver may have 1200 on/off activations while covering 318 miles during a pre-treatment shift. This study conducted in collaboration with Indiana Department of Transportation has worked with their truck upfitters to adapt geo-fenced agriculture spraying controls to seven trucks that use the Global Positioning System (GPS) position of the truck to activate the sprayer valves when the trucks enter and exit geo-fenced areas that require pre-treatment. This automated brine system enhances safety, reduces driver workload, and ensures the consistent application of brine in designated areas. Furthermore, as additional environmental constraints and reporting requirements evolve, this system has the capability of reducing application rates in sensitive areas and provides a comprehensive geo-coded application history. The Indiana Department of Transportation has scaled deployment for treating interstates and major arterials with brine. This deployment on 5500-gallon tankers, used on I-64/65/69/70/74, and 465, eliminates over 10,000 driver distraction events during every statewide pre-treatment event.

Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

Life-cycle cost profiles selection for different superstructure bridge types

During the service life of a given structure, both the initial and long-term costs are added together to provide the total cost of the bridge, resulting in a complete bridge life-cycle cost analysis (BLCCA). Historically, BLCCA has been used to select maintenance strategies but not to perform a comprehensive life-cycle cost profile (LCCP) selection. Therefore, this paper focuses on selecting cost-effective life-cycle cost profiles for different superstructure types using stochastic analysis. Life-cycle cost profile selection for different superstructures for planning purposes is still a research topic that needs development. This research aim is focused on helping to close that gap. Different maintenance and repair working actions were considered for both concrete and structural steel superstructures. Substructures were not included in the analysis. Multiple LCCPs were compared depending on the material and the superstructure type. Monte Carlo simulations were used based on probability distribution functions obtained from a historical pay item database provided by the Indiana Department of Transportation (INDOT). Cost-effective LCCPs for each superstructure type were selected based on stochastic dominance. A sensitivity analysis is also performed for each superstructure type group to identify the more impactful working actions within the life-cycle cost analysis. Life-cycle profiles that involved routine preventive working actions are more cost-effective than options that consider rehabilitation processes. The sensitivity analysis shows that the variable that most affected the results is the discount rate. When only working actions are considered, pay items related to the main structural element to have a higher impact on the mean. Cost-effective Life-cycle profiles obtained for different superstructure types can optimize the working actions scheduled for each type and be a starting point to consider and implement this method into the superstructure selection and the planning process of a project. Results presented in this paper correspond to practices and cost data in Indiana. However, the methodology can be implemented elsewhere using local data available as needed.

Case Studies of Asphalt Pavement Quality Assurance Specifications, Performance-Related Specifications, and Performance-Based Specifications

Quality assurance (QA) specifications for asphalt pavement construction measure quality characteristics to adjust payments. The measured quality characteristics are assumed to empirically relate to performance. The validity of this assumption, however, has been questioned in recent years; thus, there has been increased interest in finding ways to more directly evaluate the performance of the as-constructed materials. One proposed method to meet this need involves using the Asphalt Mixture Performance Tester (AMPT) and its associated test methods, which measure fundamental material properties that are then used to calculate the fatigue cracking performance index, Sapp, and a rutting strain index (RSI). These indices can be used in performance-related specification (PRS) or performance-based specification (PBS) frameworks to determine the pay adjustments during the construction of asphalt pavement. While PBS requires measured indices, PRS uses index-volumetrics relationships (IVRs) to predict Sapp and RSI based on conventional acceptance quality. Payment can then be based on a percent within limits (PWL) approach. This paper demonstrates how pay adjustments can be made for a paving project in Indiana following three different QA specification frameworks: the current QA specifications framework in the Indiana Department of Transportation, a PRS framework using IVRs, and a PBS framework that employs measured Sapp and RSI. For each framework, the main elements of the specification were identified and determined. The specification limits for Sapp and RSI and weight factors in composite pay factor equations were determined such that the resulting pay factors are comparable to pay factors obtained from the current QA practices.

Field Validation of Falling Weight Deflectometer Deflection-Based Critical Strain Prediction Models for Full-Depth Asphalt Pavements

Critical strains are one of the most reliable mechanistic parameters representing the structural capacity of flexible pavements. However, critical strains are currently not considered in pavement management systems (PMS) because of a lack of methodology for measuring critical strains in in-service pavements. Recently, critical strain prediction models have been developed by the Indiana Department of Transportation and Purdue University research team to estimate the critical strains of existing full-depth asphalt pavements using falling weight deflectometer (FWD) data. Even though the accuracy of these models was verified with finite element (FE) analyses, additional field validation is needed before incorporating the model results into PMS. This study focuses on validation of these previously developed critical strain prediction models using field-measured strains. Nine field sections, representing typical full-depth asphalt pavements in Indiana, were selected for study inclusion. FWD testing was conducted on the field sections and field cores were collected. Field strains were measured by a strain gauge or were determined by FE analysis using the material properties obtained from the field cores. Based on the comparative analysis, the critical strain prediction models were successfully validated with strains measured from the field. The coefficients of determination were up to 0.98 for the transverse tensile strain at the bottom of the asphalt layer and 0.93 for the vertical compressive strain at the top of the subgrade. Therefore, it is concluded that the critical strain prediction models can be used to estimate critical strains of in-service full-depth asphalt pavements for structural evaluation of PMS.

Early warning system for highway construction projects using GA-SVM

Construction project changes vis-a-vice their impact on the cost and time performance has been the main concern of practitioners and scholars for decades; hence, many attempts have been made to develop early warning mechanisms for decision-makers. Since many factors drive project changes, various strategies can be adopted to mitigate further alterations after awarding the contract. This study aimed at developing multiple Support Vector Machine (SVM) classifiers to enhance change prediction accuracy. 5,628 completed construction-oriented projects datasets comprising preconstruction information, obtained from the Indiana Department of Transportation (INDOT), were used to develop the models. All free kernel parameters were determined using Genetic Algorithm (GA) optimization. The results show that the models built using SVM classifiers with Radial Basis Function (RBF) kernel function were successful in decreasing the uncertainty associated with change order occurrence in the early phases of the projects. Therefore, the study recommends that agencies, developers, and contractors store historical project data to build early warning systems using machine learning techniques.

Calibration and Confidence in Snowplow Fleet Operations and Fleet Telematics

The Indiana Department of Transportation (INDOT) spends approximately $30 to $60 million a year on deicing salt and operates a fleet of 1,000 winter operations trucks distributed among 140 locations. The entire fleet is now instrumented with location telematics, and all new trucks have integrated dash cameras, salt spreader application rate and plow up/plow down integrated into the telematics link. When winter storms occur, they have varying regional impacts and INDOT monitors several data sources including National Weather Service (NWS) live doppler, National Severe Storms Laboratory’s (NSSL) Multi-Radar Multi-Sensor (MRMS) products, road weather monitoring stations, and connected vehicles (CV) that provide roadway segment operating speeds. This paper discusses how telematics has been integrated to provide a comprehensive view of conditions, truck asset locations, and material distribution maps. The telematics identified widely varying salt spreader rates for the same calibration settings and equipment in preliminary analysis. A calibration box is developed to allow offload calibration to occur within 10 minutes without weighing or transporting the fleet vehicle. The method is deployed across six districts at INDOT for over 1000 snowplows. A sampling of eight trucks in the fleet found the proposed calibration method reduced salt application on average of 45%. This paper describes a series of telematics dashboards for managing winter operations and details the methods developed for 140 geographically distributed truck units to conduct simple, fast, and effective calibration.

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

Construction inspection is an essential component of the quality assurance programs of state transportation agencies (STAs), and the guidelines for this process reside in lengthy textual specifications. In the current practice, engineers and inspectors must manually go through these documents to plan, conduct, and document their inspections, which is time-consuming, very subjective, inconsistent, and prone to error. A promising alternative to this manual process is the application of natural language processing (NLP) techniques (e.g., text parsing, sentence classification, and syntactic analysis) to automatically extract construction inspection requirements from textual documents and present them as straightforward check questions. This paper introduces an NLP-based method that: 1) extracts individual sentences from the construction specification; 2) preprocesses the resulting sentences; 3) applies Word2Vec and GloVe algorithms to extract vector features; 4) uses a convolutional neural network (CNN) and recurrent neural network to classify sentences; and 5) converts the requirement sentences into check questions via syntactic analysis. The overall methodology was assessed using the Indiana Department of Transportation (DOT) specification as a test case. Our results revealed that the CNN + GloVe combination led to the highest accuracy, at 91.9%, and the lowest loss, at 11.7%. To further validate its use across STAs nationwide, we applied it to the construction specification of the South Carolina DOT as a test case, and our average accuracy was 92.6%.

Leveraging Telematics for Winter Operations Performance Measures and Tactical Adjustment

The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15th, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long-term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.

Ontology-Based Knowledge Management System for Digital Highway Construction Inspection

Inspection is critical to ensuring the quality of infrastructure construction. In recent years, state highway agencies (SHAs) have been facing the challenge of a shortage of experienced inspectors due to retirements, workforce downsizing, and resignations to take jobs in the private sector. There is a critical need to retain and manage the accumulated construction inspection knowledge (what, when, and how to inspect) and integrate this knowledge into the construction business process. This paper presents an ontological approach to managing inspection knowledge and the development of a risk-based digital inspection system integrated with the construction process. The developed system consists of five knowledge compartments: inspection activities prioritized based on risk; activity-centered pay items; inspection check items; detailed checks that include priority, frequency, object, attribute, and acceptance criteria; and training materials (optional). This risk-based system is designed to align with the construction process by connecting an activity to a pay item and that pay item to a check item, resulting in an automatically generated comprehensive inspection checklist for the activity. The optional training materials are designed to train inspectors in real time on an as-needed basis. Since risk is embedded in every knowledge compartment, this system not only automates and retains the inspection knowledge but also enables SHAs to allocate their limited resources to the most critical items. This system is currently being implemented by the Indiana Department of Transportation as a digital inspection protocol integrated in its e-construction process.

Dashboards for Monitoring Congestion and Crashes in Interstate Work Zones

Work zones present challenges to safety and mobility that require agencies to balance limited resources with vital traffic management activities. Extensive literature exists regarding the impact of congestion and recommendations for work zone design to provide safe and efficient traffic operations. However, it is often infeasible or unsafe to inspect every work zone within an agency’s jurisdiction, so it is important to obtain operational feedback regarding congestion and crashes in work zones to prioritize inspection activities. This paper outlines the use of connected vehicle speed data and crash report data to identify operational performance problems in work zones. This is a way to provide feedback to queuing models used to design maintenance-of-traffic (MOT) plans. A weekly work zone report and dashboards were developed for use by the Indiana Department of Transportation (INDOT) for the purpose of assessing and improving both mobility and safety in work zones. The study has developed a mile-hours of congestion graph, frequency of speed delta heat map, congestion profile graph, and the Route Builder interactive application to comprehensively visualize work zone performance. This weekly report provides a mechanism for agency staff to maintain situational awareness of which work zones were most challenging for queues and during what periods those were likely to occur. In one case study, the reports were used to identify and mitigate operational performance problems in a work zone within 4 weeks, reducing congestion and crash rates. The integration of these data provided project managers with quantitative information about traffic mobility and performance of work zones for informed decision-making during the construction season.

Field evaluation of selected light sources for roadway lighting

Abstract The development of new lighting sources, such as light emitting diode (LED), induction, and plasma, presented more possible cost effective ways for roadway lighting. A study was therefore conducted for the Indiana Department of Transportation (INDOT) to evaluate the performance and effectiveness of some selected new lighting devices in roadway lighting. This paper describes the field evaluation process and presents the evaluation results. A number of LEDs, plasma and induction luminaires from various manufacturers were selected to replace the existing high pressure sodium (HPS) lamps in conventional and high mast lightings. Illuminance values were measured over a period of 12 months on the existing and new light sources. Light performance metrics, including illuminance level and uniformity ratios, were calculated to make quantitative comparisons of the HPS and new types of light devices. Based on the evaluation in terms of lighting performance and life cycle costs, it was concluded that LED luminaires should be utilized in roadway lighting in place of HPS luminaires. The results of this study will be useful to state highway and city street agencies in making decisions on their lighting policies and developing technical specifications for use of the new lighting technologies in roadway and street lightings. The study provides a basis for manufacturers to improve their luminaire design and integration to better fit the needs of roadway and street lightings.

Risk-Based Prioritization of Construction Inspection

In their pursuit of ensuring the quality and long-term performance of pavements, state highway agencies (SHAs) have established specifications for materials testing and construction inspection processes as critical components of their quality assurance programs. SHAs face a grand challenge, however, in the ever-growing gap between the demand for inspection and the available resources. Many strategies have been proposed over time to meet this challenge by optimizing the resource allocation process. A promising approach is to allocate limited resources to the most critical inspection items. This paper presents a novel risk-based prioritization approach for construction inspection considering quality, safety, cost, and time. Specifically, the risk is assessed in two aspects: the possibility of not meeting the specification requirements and the severity of the consequences. The scope of this study includes earthwork, concrete and asphalt pavement, and bridge decks. A comprehensive list of construction inspection items was compiled from the standard specifications and manuals of the Indiana Department of Transportation (INDOT), and narrowed down to a core set based on survey responses and interviews with INDOT domain experts. The resulting core set was aligned with the construction process, and INDOT expert opinions were sought to assess the risks and prioritize the inspection checklist. The resulting prioritized list can help INDOT and other SHAs nationwide to optimize their allocation of resources for inspection. Since all the inspection items are aligned with the construction process, implementation is straightforward: field engineers know not only what to inspect but also when to do so.

Applying Lean-Engineering Principles to Agency Business Processes to Improve Collections Associated with Infrastructure Damaged by Motor Vehicle Crashes

State and local agencies have an enormous investment in roadway infrastructure that is routinely damaged by motor vehicle crashes and must be repaired in a timely manner. Recovering the costs associated with repairing damage to state property (DSP) from responsible parties can be difficult if careful business processes are not followed by state and local agencies. In 2009, the Indiana Department of Transportation (DOT) initiated a review of its DSP business processes; a survey of peer state DSP business processes was also conducted. Subsequently, the Indiana DOT implemented a series of improvements that have increased the amount invoiced for repair of damaged roadway infrastructure from $1.4 million in FY 2010 to $8.3 million in FY 2016. During this same time period, the number of DSP invoices issued to responsible parties increased from 1,352 to 4,405. In 2008 and 2009, the overall collection rate was 51% of the invoiced amounts. Twenty-four months after implementation of new processes, the Indiana DOT improved the collection to 88% for claims under $50,000, which constitute the majority of claims filed. This paper provides a summary of the evaluation process, implementation effort, and outcome assessment metrics that can guide agencies looking for improvement opportunities in repair and invoicing activities related to damage from motor vehicle crashes.

New Approach for Screening Overweight Vehicles: Allowable Ranges of Axle Configurations

Throughout the United States, state agencies are issuing an increasingly large number of permits for overweight vehicles every year. State agencies use various methods to process permit requests. The Indiana Department of Transportation employs a screening process that consists of the Tennessee Formula (allowable weight is determined and checked, for every axle group) in combination with the gross vehicle weight limit. If a permit truck satisfies these two screening criteria, it is permitted; otherwise, it is further processed with refined analyses. However, the Tennessee Formula is based on allowable stress design. As a result, cases were observed in which the load effects produced by a truck that would be permitted by the Tennessee Formula actually exceeded load effects produced by the set of rating vehicles when the load factor rating method or the load and resistance factor rating method was employed. The set of rating vehicles is considered a minimum threshold that any nonposted bridge must be able to withstand. This paper presents a different approach for evaluating requests for overweight vehicle permits. Load effects from the set of rating vehicles and typical permit vehicles were directly compared, and screening vehicles with defined ranges of axle weights and axle spacings were developed and recommended for permitting decisions. This method is ( a) safe; ( b) simple, because it can be used easily by nontechnical personnel; and ( c) practical, because a large number of actual permit trucks fall within the proposed screening vehicles.

Current State of Reflective Cracking in the United States

AbstractReflective cracking is the most common type of distress for composite pavements that employ a hot-mix asphalt surface course over a portland cement concrete slab. Reflective cracking left untreated is known to contribute to a rougher and noisier ride and to hasten the premature failure of the surface material at or near joints. Nevertheless, the current practices of many states in the United States to manage reflective cracking are not effective due to the lack of having systematic procedures in place for not only evaluating the actual reflective cracking condition but to select the appropriate treatment methods through the decision-making process as well. To overcome the current practical issues, the Indiana Department of Transportation (INDOT) conducted a research study to develop a decision-making process to enhance treatment selection for reflective cracking in composite pavements. A survey of all state DOTs was conducted within the INDOT study to determine the current state of the practice in...

Subsurface Condition Evaluation of Asphalt Pavement for Pavement Preservation Treatments

Abstract The Indiana Department of Transportation (INDOT) has developed a guideline for evaluation of subsurface condition, a project-level evaluation for applicability of Indiana pavement preservation treatments (PPT). The developed guideline, using ground penetration radar (GPR) measurements, surface distress, and laboratory tests, determines the pavement subsurface distress severity and its distribution. The guideline also incorporates the newly developed water stripping severity test utilizing the digital image processing. The guideline has hierarchal evaluation system based on the type of data available for the test section. Level 1 is selected if GPR analysis data is available, level 2 is selected if surface distress data is available, and level 3 is used for all other cases. In addition, the subsurface distress distribution analysis tool (DCUAL) provides the locations of the PPT applicable sections. Case studies were conducted to provide aid to better understand the guideline, to present the example evaluation results upon the application of the guideline, and to validate the applicability of guidelines. Three test roads treated with PPTs within two to three years in Indiana were selected, and different evaluation levels were applied to the test roads. Level 1 and level 2 were applied for state road (SR) SR-70, and level 2 and 3 was applied for SR-257 and SR-43, respectively. Level 1 and level 2 analyses in the case study showed a good agreement with the case of the non-uniform subsurface distress distribution. In the process of determining the PPT applicability, pavements with the overall score of 60 or higher were found to be suitable for PPTs for all three levels of analysis and suitable for the Indiana State Roads having average annual daily traffic of less than 9000. Overall, the case study validated that the guideline provides a consistent, rational, and data-driven decision-making process for the applicability of the project-level pavement preservation program.

Rural Decline and Revival: State and Local Partnerships in Creating “Stellar Communities” in Rural Indiana

In recent years, public-public and public-private partnerships have become a topic of increasing interest in efforts to establish and implement holistic community revitalization initiatives. One example of this effort is Indiana’s Stellar Communities program. The program is a multi-agency partnership designed to fund comprehensive community development projects in Indiana’s smaller communities. This innovative program entails three participating state agencies, the Indiana Housing and Community Development Authority, Indiana Office of Community and Rural Affairs, and Indiana Department of Transportation. This research paper explores the progress, issues, and impacts of the Indiana Stellar Communities Program. It focuses specifically on describing program goals, planning, and implementation in the first four communities designated as “Stellar” between 2011 and 2012. In doing so, it highlights community context, best practices, and lessons learned (to date), while providing an assessment of current economic and social impacts to local communities, as well as regional implications.

Life-Cycle Cost–Based Decision Framework for Failed Portland Cement Concrete Pavement Materials in Indiana

The purpose of this study is to create a decision framework based on life-cycle cost (LCC) for failed portland cement concrete pavement (PCCP) materials in Indiana. The Indiana Department of Transportation (DOT) has a standard practice in testing and accepting the construction of PCCP. The challenge, though, is to make informed decisions concerning PCCP sections of failed materials and whether they should be removed and replaced or be accepted with a heavy penalty. This paper presents a decision framework for failed materials based on the long-term performance of PCCP and its LCC. The framework incorporates the PAVESPEC LCC module, a tool package developed as part of the performance-related specifications research efforts. The comparison of as-designed LCC and as-constructed LCC reveals the financial impact of failed PCCP materials. Probability and user confidence were incorporated to address the risk of accepting failed materials because of inferior pavement performance and shortened service life. Many simulations of various failed material scenarios were executed to develop, calibrate, and validate the decision framework. It was found that the interaction between acceptable quality characteristics (AQCs), that is, flexural strength, air content, thickness, and smoothness, had a significant impact on the LCC estimates. This framework was able to capture the composite impact of multiple AQCs deviating from their design target and provides a monetary base for the Indiana DOT to make informed decisions concerning failed PCCP materials.

Quick Determination of Freeze–Thaw Durability of Concrete Aggregates Using the Indiana Department of Transportation Hydraulic Fracture Test Equipment

The freeze–thaw durability of carbonate aggregates can vary greatly, from durable to highly susceptible to freeze–thaw distress. Using nondurable aggregate in concrete pavement exposed to freeze–thaw cycles may lead to serious distress and greatly decrease the pavement's service life. The testing needed to identify freeze–thaw durable aggregates can take several months to complete. The main objective of this study was to develop a reliable, quick test method for determining the freeze–thaw resistance of carbonate aggregates quarried in Indiana by using the hydraulic fracture test (HFT) equipment. Aggregate samples collected from 18 quarried carbonate sources that represented a range of freeze–thaw performance were subjected to HFT with the modified Indiana Department of Transportation (Indiana DOT) HFT equipment. Aggregates from the same sources were also used to produce concrete beams that were subjected to the Indiana DOT-modified AASHTO T161-B freeze–thaw test. This test evaluated the dilation of beams exposed to freeze–thaw cycles. The experimental data were analyzed statistically, and a linear regression model was developed to predict the average percent dilation of freeze–thaw test beams using parameters obtained from HFT results. Comparing the predicted dilations to the measured dilations gave an adjusted R2 value of .85 and indicated that the model has a high degree of certainty. The modified Indiana DOT HFT equipment, refined test procedures, and data analysis developed during this study are recommended as screening tools for predicting AASHTO T161–ASTM C666 test results in 8 days. However, further testing is necessary to refine and validate the model before it is fully implemented as an accepted standard.