No description available.

No description available.

Bedload transport is a complicated phenomenon in gravel-bed streams. Several factors account for this complication, including the different hydrologic regime under which different stream types operate and the wide range of particle sizes of channel bed material. Based on the hydrologic regime, there are two common types of gravel-bed streams: perennial and ephemeral. In terms of channel bed material, a gravel bed may have either unimodal or bimodal sediment. This study examines more closely some aspects of bedload transport in gravel-bed streams and proposes explanations based on fluvial mechanics. First, a comparison between perennial and ephemeral gravel-bed streams is conducted. This comparison demonstrates that under a wide range of Shields stresses, the trends exhibited by the bedload transport data of the two stream types collapse into one continuous curve, thus a unified approach is warranted. Second, an empirical bedload transport relation that accounts for the variation in the make-up of the surface material within a wide range of Shields stresses is developed. The accuracy of the relation is tested using available bedload transport data from streams with unimodal sediment. The relation is also compared against other formulae available in the literature that are commonly used for predicting bedload transport in gravel-bed streams. Third, an approach is proposed for transforming the bimodal sediment into two independent unimodal fractions, one for sand and another for gravel. This transformation makes it possible to carry out two separate computations of bedload transport rate using the bedload relation developed in this study for unimodal sediment. The total bedload transport rate is estimated by adding together the two contributions. / Ph. D.

The abilities of powerful regression and multi-type data processing allow deep learning to effectively and accurately complete multi-tasks, which is the need of civil engineering. More cases showed that deep learning has become a greatly powerful and increasingly popular tool for civil engineering. Based on these, this dissertation developed deep learning studies for the condition assessment of civil infrastructure and materials. This dissertation included five main works: (1) Deep learning and infrared thermography for asphalt pavement crack severity classification. This work focused on longitudinal or transverse cracking. This work first built a dataset with four severity levels (no, low-severity, medium-severity, and high-severity) and three image types (visible, infrared, and fusion). Then this work applied the convolutional neural network (CNN) to classify the crack severity based on two strategies deep learning from scratch and transfer learning). This work also investigated the effect of image types on the accuracy of these two strategies and on the classification of different severity levels. (2) Asphalt pavement crack detection based on convolutional neural network and infrared thermography. This work first built an open dataset with three image types (visible, infrared, and fusion) and different conditions (single, multi, thin, and thick cracks; clean, rough, light, and dark backgrounds) and periods (morning, noon, and dusk). Then this work evaluated the performance of the CNN model based on the accuracy and complexity (computational and model). (3) An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. This work considered a total of 23 factors for predicting the tensile behavior of hybrid fiber reinforced concrete (HFRC), including fibers' characteristics, mechanical properties of plain concrete, and concrete composition. Then this work compared the performance of the artificial neural network (ANN) method and the traditional equation-based method in terms of predicting the tensile stress, tensile strength, and strain corresponding to tensile strength. (4) Deep transfer learning-based vehicle classification by asphalt pavement vibration. This work first applied the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. Then this work represented the vibration signals in two different ways, including the time-domain graph and the time-frequency graph. Finally, this work proposed two deep transfer learning-based vehicle classification methods according to these two representations of vibration signals. (5) Physical-informed long short-term memory (PI-LSTM) network for data-driven structural response modeling. This work first applied the single-degree-of-freedom (SDOF) system to investigate the performance of the proposed PI-LSTM network compared with the existing methods. Then this work further investigated and validated the proposed PI-LSTM network in terms of the experimental results of one six-story building and the numerical simulation results of another six-story building. / Doctor of Philosophy / With the development of technologies, deep learning has been applied to numerous fields to improve accuracy and efficiency. More work shows that deep learning has become a greatly powerful and increasingly popular tool for civil engineering. Since civil infrastructure and materials play a dominant role in civil engineering, this dissertation applied deep learning to the condition assessment of civil infrastructure and materials. Deep learning methods were applied to detect cracks in asphalt pavements. The mechanical properties of fiber reinforced concrete were investigated by deep learning methods. Based on the asphalt pavement vibration, the type of vehicles was classified by deep learning methods. Deep learning methods were also used to investigate the structural response.

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. / Doctor of Philosophy / 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.

Moisture damage is one of the major causes of deterioration of pavements. An example is the damage caused by flooding. While the effects of pore water pressure in pavement have been studied using finite element modeling, few of the models consider a realistic moving tire and the viscoelastic behavior of the asphalt layer. Consequently, a three-dimensional finite element simulation based on Biot consolidation theory and Schapery's non-linear viscoelasticity model, was developed to accurately simulate and analyze the detrimental effects of saturated layers in asphalt pavements. In addition, a parametric study is conducted to analyze the response of pavements with varying surface and base thickness, base and subgrade permeability, and vehicle speeds under different level of saturation. The results indicate that the effects of pore water pressure be considered in the design of pavements in flood-prone areas and in the proposal of flood management plans. Ultimately, the implementation of a "flood resilient" asphalt pavement could be effective in reducing the cost of road restoration and repair in flood-prone areas. / Master of Science / Moisture damage is one of the major causes of deterioration of pavements. An example is the damage caused by flooding. While the effects of pore water pressure in pavement have been studied using finite element modeling, few of the models have accurately modeled the behavior of the asphalt concrete and have not considered the realistic loading conditions. Consequently, a three-dimensional finite element simulation was developed to accurately simulate and analyze the detrimental effects of saturated layers in asphalt pavements. In addition, a parametric study is conducted to analyze the response of pavements with varying surface and base thickness, base and subgrade permeability, and vehicle speeds under different level of saturation. The results indicate that the effects of pore water pressure be considered in the design of pavements in flood-prone areas and in the proposal of flood management plans. Ultimately, the implementation of a "flood resilient" asphalt pavement could be effective in reducing the cost of road restoration and repair in flood-prone areas.

This research effort focused on ascertaining the headlamp technology (of the eleven specified) that minimized the amount of discomfort glare and maximized the visibility of three types of pavement marking materials used in the study. Two baseline conditions, halogen low beam (HLB) and high-intensity discharge (HID) were measured both individually and in combination with three levels of UV-A. In addition, three other headlamp configurations were evaluated. Discomfort glare was measured subjectively for each headlamp configuration. Pavement marking visibility was directly measured via pavement marking detection distances. Thirty participants representing three age groups participated in this study: young (18-25 years old), middle (40-50 years old), and older (60 years and older). The headlamp technology and the pavement marking material needed to be beneficial for all age groups as all would potentially use the new technology if it were implemented in vehicles and roadways in the future. Participants evaluated discomfort glare at both a far and close distance using the nine-point DeBoer scale and evaluated pavement marking visibility by indicating when they could see the first and last pavement markings in each of the three sections. Overall, it was found that the HID configurations (HID, Middle UV-A + HID, High UV-A + HID) with a sharp cut-off beam pattern provided the least amount of discomfort glare. In contrast, the halogen configurations (HLB, Hybrid UV-A + HLB, Middle UV-A + HLB, High UV-A + HLB) and high output halogen with a straight-ahead beam pattern provided the longest detection distances. Two of the pavement markings: a two part liquid system (developed by 3M) and a fluorescent paint provided longer detection distances than a thermoplastic marking. / Master of Science

Since World War II, the military has sought methods for rapid stabilization of weak soils for support of its missions worldwide. Over the past 60 years, cement and lime have consistently been found to be among the most effective stabilizers for road and airfield applications, although recent developments show promise using nontraditional stabilizers. The purpose of this research is to determine the most effective stabilizers and dosage rates of stabilizers to increase the strength of soft clay soils (initial CBR = 2) within 72 hours for contingency airfields to support C-17 and C-130 aircraft traffic. Pavement design charts for various aircraft loading conditions were generated using the Pavement-Transportation Computer Assisted Structural Engineering Program, which was developed by the Engineering Research and Development Center to determine ranges of required strength and thickness for an underlying subbase layer and a top base layer, such as stabilized soil, crushed-aggregate, or aluminum matting. From laboratory studies, the required design strengths for many loading conditions were achieved by treating clay with 2%-4% pelletized quicklime for the underlying subbase layer, and treating clay with 2%-4% pelletized quicklime, 1% RSC15 fibers, and 11% Type III cement for the top base layer. While the base layer requires a minimum thickness of six inches, the required subbase layer thickness is often quite large and may be difficult to construct. However, newly developed construction equipment currently used for subgrade stabilization on civilian projects should be able to stabilize the soil down to these large required depths and make construction possible. / Master of Science

Doctor of Philosophy / Department of Civil Engineering / Mustaque A. Hossain / Remaining service life (RSL) has been defined as the anticipated number of years that a pavement will be functionally and structurally acceptable with only routine maintenance. The Kansas Department of Transportation (KDOT) has a comprehensive pavement management system, network optimization system (NOS), which uses the RSL concept. In support of NOS, annual condition surveys are conducted on the state highway system. Currently KDOT uses an empirical equation to compute RSL of flexible pavements based on surface condition and deflection from the last sensor of a falling-weight deflectometer (FWD). Due to limited resources and large size, annual network-level structural data collection at the same rate as the project level is impractical. A rolling-wheel deflectometer (RWD), which measures surface deflections at highway speed, is an alternate and fast method of pavement-deflection testing for network-level data collection. Thus, a model that can calculate RSL in terms of FWD first sensor/center deflection (the only deflection measured by RWD) is desired for NOS. In this study, RWD deflection data was collected under an 18-kip axle load at highway speed on non-Interstate highways in northeast Kansas in July 2006. FWD deflection data, collected with a Dynatest 8000 FWD on the KDOT network from 1998 to 2006, were reduced to mile-long data to match the condition survey data collected annually for NOS. Normalized and temperature-corrected FWD and RWD center deflections and corresponding effective structural numbers (SNeff) were compared. A nonlinear regression procedure in Statistical Analysis Software (SAS) and Solver in Microsoft Excel were used to develop the models in this study. Results showed that FWD and RWD center deflections and corresponding SNeff are statistically similar. Temperature-correction factors have significant influence on these variables. FWD data analysis on the study sections showed that average structural condition of pavements of the KDOT non-Interstate network did not change significantly over the last four years. Thus, network-level deflection data can be collected at four-year intervals when there is no major structural improvement. Results also showed that sigmoimal relationship exists between RSL and center deflection. Sigmoidal RSL models have very good fits and can be used to predict RSL based on center deflection from FWD or RWD. Sigmoidal equivalent fatigue crack-models have also shown good fits, but with some scatter that can be attributed to the nature and quality of the data used to develop these models. Predicted and observed equivalent transverse-crack values do not match very well, though the difference in magnitude is insignificant for all practical purposes.

Master of Science / Department of Civil Engineering / Mustaque A. Hossain / The objective of this research project was to assess the condition of general aviation airport pavements in Kansas. The study was also intended to form the basis for a pavement management system (PMS). A total of 137 runways from 107 airports across the state were surveyed. MicroPAVER, a PMS system developed by the U.S. Army Corps of Engineers, was selected as the platform for the PMS. An inventory database was developed for all runways in the network. Information about the construction and maintenance history was entered into the MicroPAVER database. On-site surveys were conducted between the months of May and July of 2008 to assess pavement conditions in terms of the Pavement Condition Index (PCI), following the methodology outlined by ASTM D 5340-04 and adopted by the Federal Aviation Administration (FAA). Approximately 68% of the sections surveyed were in “good” to “satisfactory” condition. Almost one-third of the network can be rated as “good.” About 21% of the sections studied were in “fair” condition. Overall, the condition of the network can be rated as “satisfactory.” A condition prediction curve was developed for each of the two different types of surfaces. From the prediction curves created using MicroPAVER, it was estimated that the number of branches rated as “good” could decrease by 50% by 2010. As much as 44% of the network could have a rating of “fair” by 2013 if the sections receive only routine maintenance. Two budget scenario comparison reports developed show that the 108 runways of the 78 general aviation airports eligible for FAA funding in Kansas could be brought to a “satisfactory” rating or above (i.e. average PCI ≥ 70) by spending approximately $15 million on average per year for the next five years.