Use of the Traffic Speed Deflectometer for Concrete and Composite Pavement Structural Health Assessment: A Big-Data-Based Approach Towards Concrete and Composite Pavement Management and Rehabilitation
| dc.contributor.author | Scavone Lasalle, Martin | en |
| dc.contributor.committeechair | Flintsch, Gerardo W. | en |
| dc.contributor.committeemember | Brand, Alexander S. | en |
| dc.contributor.committeemember | Katicha, Samer Wehbe | en |
| dc.contributor.committeemember | Diefenderfer, Brian Keith | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2022-08-24T08:00:11Z | en |
| dc.date.available | 2022-08-24T08:00:11Z | en |
| dc.date.issued | 2022-08-23 | en |
| dc.description.abstract | The latest trends in highway pavement management aim at implementing a rational, data-driven procedure to allocate resources for pavement maintenance and rehabilitation. To this end, decision-making is based on network-wide surface condition and structural capacity data – preferably collected in a non-destructive manner such as a deflection testing device. This more holistic approach was proven to be more cost-effective than the current state of the art, in which the pavement manager grounds their maintenance and rehabilitation-related decision making on surface distress measurements. However, pavement practitioners still rely mostly on surface distress because traditional deflection measuring devices are not practical for network-level data collection. Traffic-speed deflection devices, among which the Traffic Speed Deflectometer [TSD], allow measuring pavement surface deflections at travel speeds as high as 95 km/h [60 miles per hour], and reporting the said measurements with a spatial resolution as dense as 5cm [2 inches] between consecutive measurements. Since their inception in the early 2000s, and mostly over the past 15 years, numerous research efforts and trial tests focused on the interpretation of the deflection data collected by the TSD, its validity as a field testing device, and its comparability against the staple pavement deflection testing device – the Falling Weight Deflectometer [FWD]. The research efforts have concluded that although different in nature than the FWD, the TSD does furnish valid deflection measurements, from which the pavement structural health can be assessed. Most published TSD-related literature focused on TSD surveys of flexible pavement networks and the estimation of structural health indicators for hot-mix asphalt pavement structures from the resulting data – a sensible approach given that the majority of the US paved road pavement network is asphalt. Meanwhile, concrete and composite pavements (a minority of the US pavement network that yet accounts for nearly half of the US Interstate System) have been mostly neglected in TSD-related research, even though the TSD has been deemed a suitable device for sourcing deflection data from which to infer the structural health of the pavement slabs and the load-carrying joints. Thus, this Dissertation's main objective is to fulfill this gap in knowledge, providing the pavement manager/practitioner with a streamlined, comprehensive interpretation procedure to turn dense TSD deflection measurements collected at a jointed pavement network into characterization parameters and structural health metrics for both the concrete slab system, the sub-grade material, and the load-carrying joints. The proposed TSD data analysis procedure spans over two stages: Data extraction and interpretation. The Data Extraction Stage applies a Lasso-based regularization scheme [Basis Pursuit coupled with Reweighted L1 Minimization] to simultaneously remove the white noise from the TSD deflection measurements and extract the deflection response generated as the TSD travels over the pavement's transverse joints. The examples presented demonstrate that this technique can actually pinpoint the location of structurally weak spots within the pavement network from the network-wide TSD measurements, such as deteriorated transverse joints or segments with early stages of fatigue damage, worthy of further investigation and/or structural overhaul. Meanwhile, the Interpretation Stage implements a linear-elastic jointed-slab-on-ground mathematical model to back-calculate the concrete pavement's and subgrade's stiffness and the transverse joints' load transfer efficiency index [LTE] from the denoised TSD measurements. In this Dissertation, the performance of this back-calculation technique is analyzed with actual TSD data collected at a 5-cm resolution at the MnROAD test track, for which material properties results and FWD-based deflection test results at select transverse joints are available. However, during an early exploratory analysis of the available 5-cm data, a discrepancy between the reported deflection slope and velocity data and simulated measurements was found: The simulated deflection slopes mismatch the observations for measurements collected nearby the transverse joints whereas the measured and simulated deflection velocities are in agreement. Such a finding prompted a revision of the well-known direct relationship between TSD-based deflection velocity and slope data, concluding that it only holds on very specific cases, and that a jointed pavement is a case in which deflection velocity and slope do not correlate directly. As a consequence, the back-calculation approach to the pavement properties and the joints' LTE index was implemented with the TSD's deflection velocity data as input. Validation results of the back-calculation tool using TSD data from the MnROAD low volume road showed a reasonable agreement with the comparison data available while at the same time providing an LTE estimate for all the transverse joints (including those for which FWD-based deflection data is unavailable), suggesting that the proposed data analysis technique is practical for corridor-wide screening. In summary, this Dissertation presents a streamlined TSD data extraction and interpretation technique that can (1) highlight the location of structurally deficient joints within a jointed pavement corridor worthy of further investigation with an FWD and/or localized repair, thus optimizing the time the FWD spends on the road; and 2) reasonably estimate the structural parameters of a concrete pavement structure, its sub-grade, and the transverse joints, thus providing valuable data both for inventory-keeping and rehabilitation management. | en |
| dc.description.abstractgeneral | When allocating funds for network-wide pavement maintenance, such as the State or Country level, the engineer relies on as much pavement condition data as possible to optimally assign the most suitable maintenance or rehabilitation treatment to each pavement segment. Currently, practitioners rely mostly on surface condition data to decide on how to maintain their roads, as this data can be collected fast and easily with automated vehicle-mounted equipment and analyzed by computer software. However, managerial decisions based solely on surface condition data do not optimally make use of the Agency resources, for they do not precisely account for the pavements' structural capacity when assigning maintenance solutions. As such, the manager may allocate a surface treatment on a structurally weak segment with a poor surface which will be prone to an early failure (thus wasting the investment) or, conversely, reconstruct a deteriorated yet strong segment that could be fixed with a surface treatment. The reason for such a sub-optimal managerial practice has been the lack of a commercially-available pavement testing device capable of producing structural health data at a similar rate as the existing surface scanning equipment – pavement engineers could only appeal to crawling-speed or stop-and-go deflection devices to gather such data, which are fit for project-level applications but totally unsuitable for routine network-wide surveying. Yet, this trend reverted in the early 2000s with the launch of the Traffic Speed Deflectometer [TSD], a device capable of getting dense pavement deflection measurements (spaced as close as 5cm [2 inches] between each other) while traveling at speeds higher than 50 mph. Following the device's release, numerous research activities studied its feasibility as a network-wide routine data collection device and developed analysis schemes to interpret the collected measurements into pavement structural condition information. This research effort is still ongoing, the Transportation Pooled Fund [TPF] Project 5(385) is aimed in that direction, and set the goal of furnishing standards on the acquisition, storage, and interpretation of TSD data for pavement management. This being said, data collection and analysis protocols should be drafted to interpret the data gathered by the TSD on flexible and rigid pavements. Concerning TSD-based evaluation of flexible asphalt pavements, abundant published literature discussing exists; whereas TSD surveying of concrete and composite (concrete + asphalt) pavements has been off the center of attention, partly because these pavements constitute only a minority of the US paved highway network – even though they account for roughly half of the Interstate system. Yet, the TSD has been found suitable to provide valuable structural health information concerning both the pavement slabs and the load-bearing joints, the weakest element of such structures. With this in mind, this Dissertation research is aimed at bridging this existing gap in knowledge: a streamlined analysis methodology is proposed to process the TSD deflection data collected while surveying a jointed rigid pavement and derive important structural health metrics for the manager to drive their decision-making. Broadly speaking, this analysis methodology is constituted by two main elements: • The Data Extraction stage, in which the TSD deflection data is mined to both clear it from measurement noise and extract meaningful features, such as the pulse responses generated as the TSD travels over the pavement joints. • The Interpretation stage, which is more pavement engineering-related. Herein, the filtered TSD measurements are utilized to fit a pavement response model so that the pavement structural parameters (its stiffness, the strength of the sub-grade soil, and the joints' structural health) can be inferred. This Dissertation spans both the mathematical grounds for these analysis techniques, validation tests on computer-generated data, and experiments done with actual TSD data to test their applicability. The ultimate intention is for these techniques to eventually be adopted in practice as routine analysis of the TSD data for a more rational and resource-wise pavement management. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35394 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/111606 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | en |
| dc.subject | TSD | en |
| dc.subject | concrete | en |
| dc.subject | pavement | en |
| dc.subject | joints | en |
| dc.subject | management | en |
| dc.subject | big-data | en |
| dc.title | Use of the Traffic Speed Deflectometer for Concrete and Composite Pavement Structural Health Assessment: A Big-Data-Based Approach Towards Concrete and Composite Pavement Management and Rehabilitation | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |