Nondestructive Evaluation of Highway System Asset Foundational Condition and Capability (2023)

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63   Introduction To identify the gaps in the current and emerging technologies, the maturity of all technologies was evaluated based on a procedure developed specifically for NDE technologies used to evaluate foundational conditions of transportation assets. Several models have been developed and deployed in different scientific and engineering fields to assess technological maturity and readiness. Several published studies assessed the benefits and limitations of different models used in infrastruc­ ture condition assessment and monitoring. However, there was no coherent process established specifically to assess the level of technology maturity for infrastructure assessment applications. A valid process should not dictate the acceptance or rejection of research and development activities, rather it should help to better understand and assess the gaps that should be addressed during technology development and deployment. One commonly used method to assess tech­ nology maturity is the technology readiness levels (TRL) developed by the American National Aeronautics and Space Administration (NASA) and deployed by several government agencies and professional associations (Sadin, Povinelli, and Rosen 1989; Héder 2017). Technology Maturity Model In this study, a new maturity model and assessment process were developed based on the con­ cepts presented in the TRL method (Graettinger et al. 2002). The process applies the maturity scales shown in Tables 5­1 and 5­2. The developed scale is divided into two parts: six technology devel­ opment maturity levels (TDMLs) and three technology implementation maturity levels (TIMLs). A TDML is composed of three components, namely hardware (HW) maturity, engineering fun­ damentals (EF) maturity, and software (SW) maturity. The EF maturity component is added to address the unique nature of transportation infrastructure and the significant uncertainties and variability between design assumptions and in situ conditions. Although typical technology development and implementation progress sequentially through the levels described in Tables 5­1 and 5­2, there are exceptions. Some technologies may reach a high level of maturity without going through all lower levels or may have different maturity levels for the individual components. The process can typically be iterative, where the limitations identified at a high maturity level may suggest the technology revert to a lower maturity level to address the iden­ tified limitations. During the technology maturity evaluation process, a technology is said to achieve a specific maturity level when it satisfies the corresponding outcomes outlined in Tables 5­1 and 5­2. The overall TDML corresponds to the minimum score among the HW, EF, and SW maturity scores. Due to the different levels of sophistication of NDE technologies, the descriptions in Tables 5­1 and 5­2 are generic, which allows evaluators to use their judgment and specify the requirements appli­ cable to these different technologies. In addition to the general maturity scale outlined in Table 5­1, C H A P T E R   5 Nondestructive Evaluation Technology Maturity and Gap Assessment

TDML Description 1. Basic principles observed and reported. HW: Initial concepts of the different components and processes needed to acquire measurements defined. Examples may include flow charts of the measurement process, layout of core components and their workflow, or a simulated prototype. EF: Lowest level of technology readiness. Scientific research begins to be translated into applied research and development. Examples include peer-reviewed publications and white papers on a technology’s basic properties and fundamental concepts relating crude measurements to infrastructure target parameter(s) with possibly sample measurements acquired using predecessor equipment (for example, electrical resistivity correlation with material density). SW: Lowest level of software readiness. Basic development of hard-coded models or calculation procedures to process the acquired or simulated crude device outputs. Examples include spreadsheets to perform basic arithmetic on crude measurements to estimate a trace of target parameters. 2. Component and/or proof-of-concept prototype development and “low fidelity” evaluation of measurement to parameter association. HW: Basic technological components and/or proof-of-concept prototype developed and demonstrated in a controlled environment. Examples may include measuring crude data for elements with known and designed parameters in a controlled environment with controlled geometry and material characteristics. EF: Initial data is observed with a “low fidelity” trend assessment for a few demonstration cases in a controlled environment such as laboratory-scale models or controlled field experiments. SW: Overall processing algorithms are defined, and a first draft code is executed to automatically process acquired measurements. The codes are not calibrated and are mostly hard coded with coefficients assumed subjectively by observing the overall trends between the crude measurements and the target parameters. 3. Assessment of prototype repeatability and reproducibility. HW: Improve and develop the prototype further and demonstrate its repeatability under similar testing conditions for the same cases and reproducibility of measurements by different operators or multiple copies of the proof-of-concept prototype. EF: Accuracy and precision assessment of acquired measurements to quantify the output quality and the statistical characteristics of the measurements. The findings are representative of the controlled testing environment and do not necessarily apply to the operation environment. SW: Validate the code’s ability to reproduce the same outputs for the same inputs on different machines and produce consistent outputs using different inputs. 4. Demonstration of a prototype in a selected, low-variability operational environment and “high-fidelity” evaluation of measurement to parameter association. HW: Refine the prototype to work as a unit in low-variability operational environment. The technology is demonstrated and tested on low-variability, full- scale infrastructure. EF: An experimental plan is executed to derive a high-fidelity quantitative model(s) that describes the association between the crude measurements and target parameters. SW: Software releases are alpha versions and configuration control is initiated. Verification, validation, and accreditation (VV&A) are initiated. 5. System refinement and recommended procedure development. HW: Fidelity of the system/prototype increases significantly. The system is refined to address different operational conditions, and add-ons are developed and integrated to capture additional measurements and filters that improve the system output and versatility. EF: Quantitative model(s) that describe the association between the crude measurements and target parameters refined and model coefficients fine-tuned to improve the model accuracy. Fully developed, recommended testing and analysis procedures. SW: A software interface developed and fully integrated with the hardware system. The reliability of the software ensemble increases significantly. User guides developed with a full description of processing procedures. 6. Technology assessment in a wide range of operational conditions. HW: Technology has been proven to work in its final form and under expected conditions. Case studies and pilot projects include the testing of a wide range of assets similar to routine evaluation processes. EF: Models are improved to derive more generic conclusions applicable to most conditions and assets encountered. Enhanced understanding of the measurements to parameter association and the technology limitations. SW: Major software bugs fixed. Additional software features and analytical capabilities are added. Improvements to handle larger data during routine data collection are addressed. Table 5-1. High-level description of TDMLs and their constituting components.

Nondestructive Evaluation Technology Maturity and Gap Assessment 65 TIML Description 1. Assessing the role and impact of technology on overall evaluation procedures. Reassess the role of measured characteristics and parameters in infrastructure condition assessment, performance evaluation and prediction, and infrastructure management and monitoring. Examples may include the use of parameters/measurements in condition indices and performance modeling and forecasting. This can include an ROI analysis at the individual infrastructure management and assessment level. 2. Assessing the benefits and ROI of technology on routine transportation network evaluation and management. Assess the network- or agency-level benefits of incorporating technology in agency practices. The extent of the benefits varies between technologies based on the extent of the infrastructure element to be assessed using the new technology. Examples may include network-level ROI analysis, evaluating the improvement in network condition and performance given the economic constraints, safety evaluation, and serviceability improvements. Note the benefits may reside on different dimensions including, but not limited to, safety, economic, travel time, and user satisfaction. 3. Integrating technology with established processes and practices. After demonstrating the agency-level benefits, technology deployment and outputs are integrated with established practices to improve the network performance and the state of practice. Table 5-2. High-level description of TIMLs and their constituting components. a list of typical activities for TDMLs is found in Appendix B to provide general guidelines in a checklist format. The tables are designed to help in assessing the gaps in the development stages. No activities were provided for the TIMLs because these activities can differ vastly based on the agencies’ practices, needs, and the developed technology. The outlined activities are not exhaustive and are not necessarily applicable to all technologies. The activities for TDMLs are categorized based on their use as before—hardware, engineering fundamentals, or software devel­ opment. Some activities can serve multiple categories. To perform a detailed technology maturity evaluation, it is recommended to start with a high­ level maturity assessment using Tables 5­1 and 5­2 and then perform a more detailed assessment of the gaps at the partially completed level using the tables in Appendix B. The assessment should be performed for the technology within a specific transportation asset context since the same technology may achieve different maturity levels depending on the application. To assign a semi­ quantitative index for the percentage of completion of maturity levels, the activities are given a binary score, where zero (0) indicates an incomplete activity and unity (1) indicates a fully executed activity. Partial points can be given if the activity is complex and includes sub­activities. To mini­ mize or eliminate the use of partial scores during the assessment, overly sophisticated activities can be broken down into two or more activities. If partial scores are necessary, a score of 0.5 is used to indicate partially completed activities. Evaluators can omit or add applicable activities to the lists in Appendix B depending on the evaluated technology. If activities are added or omitted the evalu­ ator should properly document the justification for it and adjust the total scores accordingly. Using the total scored points, the percent complete score (PCS) is calculated as the sum of scored points divided by the total weights for each category and multiplied by a hundred. The PCS should be calculated for each category (HW, EF, SW) separately. The definitions and terms used in the development of the technology maturity assessment are as follows: • Technology: The sum of consistent techniques, methods, processes, and tools used to observe or measure a characteristic or response of an infrastructure feature or the surrounding geomaterial supporting the infrastructure. • Association: A general term describing the statistical relationship between two variables.

66 Nondestructive Evaluation of Highway System Asset Foundational Condition and Capability • Crude measurement: The reading a technology acquires before processing into a parameter. Typically relates to electronic signals such as resistivity readings, dielectric permittivity, and vibration responses. • Target parameter: An observable value or characteristic of interest that relates to the infra­ structure condition. • Predecessor equipment: Research equipment that can perform equivalent tasks to the system components within a controlled environment such as a laboratory or small­scale field demonstra­ tion with uniform conditions. • Proof-of-concept prototype: System of integrated components configured to perform in a controlled environment and resembles the final system in function only; however, it lacks the full suite of components to perform in the relevant or operational environment. • Prototype: A full­scale model used to evaluate the technical or manufacturing feasibility of a particular technology or process, concept, end item, or system. • High fidelity: Reliable quantitative or scientific description of the association between a crude measurement and the observable parameter or characteristic. • Low fidelity: Representative of the overall trend and expected strength of association between multiple observable variables or parameters. Has limited ability to provide anything but first­ order information about the product. • Controlled environment: A highly controlled testing environment with high control over the variability affecting the testing outcome. Examples include laboratory testing or small­scale field demonstration with individual infrastructure components. • Relevant environment: A testing environment that simulates the key aspects of the operational environment. • Operational environment: An environment that addresses all the operational requirements and specifications required of the final system. The maturity assessment of the technologies identified in Chapter 4 is detailed in Appendix C. Some of the technologies identified in Chapter 4 were excluded from the table because they are old or legacy technologies that have a very limited possibility of improvement, or they are at a very early stage and only reported in isolated studies with no sufficient evidence of their applicability for future widespread application. It is important to note that the maturity scores are based on the technology’s applicability to the asset type and the corresponding application. Therefore, some of the mature technologies may be scored at a lower maturity level because they need enhancement for that specific application. Gap Assessment A gap in technology maturity can be defined as the difference between the current maturity level and the highest maturity level to be achieved. Therefore, the maturity assessment provided in Appendix C captures the gaps in the technologies. In addition to the scores, comments were added to the maturity assessment table in Appendix C to explain some of the specifics related to the gaps and the potential for the technology to improve the foundation condition assessment capabilities if developed appropriately. Table 5­3 shows the technologies identified as having a significant potential to improve the foundational condition assessment capabilities if developed appropriately. The technologies have a wide range of TDML and TIML, and therefore, the gaps can be in the TDML, the TIML, or both. Other technologies that were not ranked as having significant improvement potential are either sufficiently mature, or there are no significant benefits if they are further developed. Alter­ natively, they are at an early stage of development and need additional studies to reach a point where their benefits can be fully developed. The specific gaps and potential problem statements corresponding to the technologies are shown in the “Comments” column of the table.

Nondestructive Evaluation Technology Maturity and Gap Assessment 67 Technology Asset TDML TIML Comments HW EF SW Traffic speed deflectometer devices (TSDDs) Pavements 3 3 1 1 HW: Due to the limitations of EF and SW the designs can be improved to infer better parameters. EF: There is a need to improve the models inferring the layer parameters from the pavement response to a moving load. SW: The software only provides a data output, but there is limited knowledge on how to use the data and incorporate it to predict and assess the foundational capacity of pavements. The device presents great potential for network data collection. Potential problem statement: Improvement of Analysis of TSDDs to assess pavement foundational capacity and integration with asset management practices. Surface waves analysis Pavements 3 6 3 1 HW: There is no automated system. The data collection can be time-consuming and needs specialized training. The procedure and how the test is performed can impact the reliability of the results. SW: Interpretation of the results and the extract parameters need improvement. TIML (layer thickness): Reliability depends on the contrast in the properties between the different layers. GPR is a better tool and can be more sensitive to the different layers. Potential problem statement: Work on the processing and interpretation. LiDAR MSE Walls 5 6 4 1 The analysis procedure and software tools can be improved and automated. Data management can be challenging; however, more solutions are available and are being developed. Potential problem statement: Relate deformations measured using LiDAR to changes in geomaterial and foundation material support. Embedded sensors for material deterioration rate Bridges 2 2 1 1 Potential problem statement: Improve the interpretation and use of corrosion rates on the deterioration and foundational capacity of assets. Pressure and strain sensors Bridges 6 6 5 1 Potential problem statement: How to install sensors and use them to monitor for extreme events and extreme loads. Table 5-3. Maturity and gap assessment of NDE technologies with significant potential impact on foundational condition assessment capabilities. (continued on next page)

68 Nondestructive Evaluation of Highway System Asset Foundational Condition and Capability Table 5-3. (Continued). Technology Asset TDML TIML Comments HW EF SW Tilt sensors and accelerometers Signs and masts foundations 6 6 6 1 Very limited to no mention of NDE evaluation for foundations. Potential problem statement: Use of sensors to assess the impact of repeated lateral loads on foundational conditions. DFOS Cantilever and ground anchor walls 4 3 1 1 There is a gap in the procedures and software tools to assess the condition of acquired data. Potential problem statement: Improve the analytical procedures to assess the condition of retaining systems. DFOS Soil cut slopes 4 3 1 1 There is a gap in the procedures and software tools to assess the condition of acquired data. Potential problem statement: Improve the analytical procedures to assess the condition of retaining systems. High-resolution fiber optic sensor Tunnels 4 3 1 1 Potential problem statement: Use of fiber optic sensors to monitor deformation and related foundation conditions. The following examples illustrate the research team’s rationale. Generally, LiDAR is well­ developed and mature from a technology development standpoint. The needed improvements are related to the use of the measurements to monitor retaining structures over time and assess the potential changes in geomaterial and foundation material support. Pressure and strain sensors are well­developed and mature from the technology development standpoint. The needed improve­ ments are related to their use to monitor the impacts of extreme events and extreme loads. Signs and masts were not ranked as high­risk assets, and the use of tilt sensors and acceler­ ometers was not identified in the literature as a tool used to assess and monitor the performance of these assets. Based on the research team’s experience, however, there is potential to use these simple technologies to monitor the impact of repeated lateral loads, especially wind loads, on the condition of the foundations and the potential reduction of lateral resistance due to repeated lateral loading/material softening. Both technologies—tilt sensors and accelerometers—are well­ developed and mature from a technology development standpoint.