Showing papers by "Imad L. Al-Qadi published in 2023"

Mechanistic Excess Fuel Consumption of a 3D Passenger Vehicle on Rough Pavements

Abstract: A passenger vehicle’s fuel-powered engine compensates for its dissipated energy, which is caused by road roughness. This paper presents an integrated vehicle–pavement approach and investigates the excess fuel consumption of a seven-degree-of-freedom (DOF) full-car model on rough pavements. In the approach, a nonstationary Laplace process is used to artificially generate road roughness profiles with consideration of local roughness variance and parallel roughness correlation. The proposed mechanistic approach quantifies the impact of road roughness and vehicle dynamic characteristics on excess fuel consumption. The study concluded that overlooking local roughness variance, an indicator of road roughness nonstationarity, may underestimate excess fuel consumption by 30%. Compared with a two-dimensional (2D) half-car model, a three-dimensional (3D) full-car model reduces computation error of excess fuel consumption by approximately 11%. For practical implementation, the extended roughness-speed-impact (ERSI) model (R2=98%) is developed to estimate roughness-induced energy dissipation of five two-axle vehicles.

Model development for moisture content and density prediction for non-dry asphalt concrete using GPR data

Abstract: Ground-penetrating radar (GPR) is a non-destructive testing technique used to assess various civil structures, including pavements. It may be applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Because moisture may exist in in-service AC pavement layers and hinder the density prediction, quantifying moisture content in AC would improve its layer-density prediction. In addition, quantifying moisture content of cold recycled pavements would allow monitoring of the curing process of the treatment. Hence, the proper time for opening roads to traffic and/or placing an overlay could be identified. In this study, data were collected from both field cold-recycling projects and laboratory test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes. The Al-Qadi–Cao–Abufares (ACA) model was derived based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi–Lahouar–Leng (ALL) model; it incorporates the effect of moisture on the bulk dielectric constant to predict density of non-dry AC. The model predicts AC density with an average error of 2% and also predicts moisture content with a root mean square error of 0.5%.

Smart Mobility Blueprint for Illinois

Abstract: Connected, automated, shared, and electric (CASE) technologies have invoked Mobility 4.0—a connected, digitized, multimodal, and autonomous system of systems. This project established a flexible and adaptable blueprint that would streamline multidisciplinary and multistakeholder efforts as well as leverage available resources to prepare the Illinois Department of Transportation and other transportation agencies. Illinois has several strengths that make it an attractive location for CASE technology companies, including a talent pool from top-ranked universities, well-developed transportation infrastructure, government support, and a robust ecosystem of collaboration and innovation. Illinois also faces potential challenges (e.g., competition from other states and countries, limited access to funding, regulatory hurdles, and infrastructure readiness for new mobility technologies). Seven smart mobility pillars were identified in this study for Illinois—namely, connected and automated (CA) freight, scaling intelligent transportation systems, farm automation, insurance, urban mobility, CA logistics, and alternative fuels. The balanced scorecard ranked the pillars as follows (from highest): alternative fuels, scaling intelligent transportation systems, CA freight, farm automation, CA logistics, insurance, and urban mobility. Tactical focus areas were also identified per pillar and were prioritized with suggested leads and stakeholders to champion the CASE directives and opportunities. Near-term actions for Illinois were also suggested that included establishing a central structure for Illinois’ CASE program, enriching the knowledge base and experience, preparing transportation infrastructure, partnerships with external stakeholders, and expansion of laws, regulations, and policies that will help administer and grow CASE technology deployment and integration.

Asphalt Concrete Mix Design Optimization Using Autoencoder Deep Neural Networks

Abstract: Asphalt concrete (AC) balanced mix design (BMD) is based on the selection of aggregate gradation, component volumetrics, and binder content to control pavement cracking and rutting potential. The Illinois Flexibility Index Test (I-FIT) and the Hamburg Wheel Tracking Test (HWTT) results, used to predict cracking and rutting potential, respectively, are used in the BMD approach. However, BMD generally relies on a trial-and-error process to identify the aggregate gradation and binder content needed to meet volumetrics and optimize I-FIT and HWTT results. Minimizing or eliminating the trial-and-error process would increase productivity and accuracy. Therefore, this study proposes an autoencoder deep neural network (ADNN) to develop optimized AC mix design alternatives that can meet a prescribed flexibility index (FI) and rut depth (RD). Autoencoders are a type of neural network designed for representation learning composed of an encoder and a decoder. The encoder detects a structured pattern in the original input data to create a compressed representation of the AC mix design. The decoder reconstructs the compressed representation. The proposed autoencoder is composed of an encoder of five hidden layers, a latent space of one node, and a five-hidden-layer decoder. Models were created from a database of 5,357 data sets that include mix properties, I-FIT FI, and HWTT RD (after data preprocessing was conducted). An autoencoder was then trained to predict the total binder content, and aggregate gradation based on a target mix type, FI, and RD.

Incorporation of Pavement Dynamic Loading in Mechanistic–Empirical Design Frameworks

Abstract: A framework for incorporating dynamic loading in pavement design is introduced. The framework is composed of five main steps. Firstly, input parameters, such as traffic characteristics, material properties, and the pavement configuration, are determined. Both primary and secondary data were obtained from the literature in this step. Secondly, a database of critical pavement responses, used by AASHTOWare software’s transfer functions to calculate pavement damage, was built using advanced pavement finite element models after the loading input was defined. The database includes various pavement configurations, axle configurations (single and tandem), pavement material properties, temperatures, loading conditions, and tire types (single steering, dual, and wide-base). AASHTOWare’s transfer function predicts pavement damage from pavement responses and is used to calculate the international roughness index (IRI) in the third and fourth steps, respectively. Finally, the load spectrum is adjusted to incorporate roughness-induced dynamic loading once the IRI exceeds 95 in./mi. A simple analytical dynamic-loading model was developed, based on mechanistic truck–trailer results. The model is a function of the tire configuration, speed, and IRI. Multiple case studies were performed, considering various pavement configurations, material properties, average annual daily traffic values, and dynamic-loading percentiles. Results showed that dynamic loading had the most significant impact on fatigue life, followed by rutting potential. The AASHTOWare’s transfer functions were found to be insensitive to an increase in loading when considering thick-pavement structures. The outcome of this effort was validated using the measured and predicted IRI from the Specific Pavement Study sections, which are part of the Long-Term Pavement Performance program.

Environmental and Economic Efficiency of Stone-Matrix Asphalt with Surface Micro-Milling

Abstract: The Georgia Department of Transportation (GDOT) has employed a combination of open-graded friction course (OGFC) and stone-matrix asphalt (SMA) layers on its Interstate highways for more than two decades. Given that the SMA layer was structurally intact, GDOT implemented micro-milling to solely remove the OGFC layer. Four conventional resurfacing cycles were considered to compare the environmental and economic impacts for a section of Interstate 95 using life-cycle assessment and life-cycle cost analysis, assuming 8-, 10-, 12-, and 14-year resurfacing intervals of asphalt concrete (AC) resurfacing. Within the pavement life cycle, the maintenance and rehabilitation (M&R), and the use stages were significantly influenced by the change of scenarios. The M&R stage revealed a twofold cost increase for the full-depth AC, as compared with SMA, over the pavement life, because of the latter’s relative improved structural performance and a delay in future rehabilitation. Furthermore, in the use stage, M&R affected the agency costs significantly, whereas the work-zone delay governed the user cost. The holistic evaluation of the SMA and micro-milling combination revealed positive outcomes in field performance, along with environmental and economic impacts. However, it is worth noting that the scenarios presented follow GDOT’s approach with a long-standing use of SMA and micro-milling. Given the extensive experience with the aforementioned combination, it was evident that the method provided significant benefits, especially in the long-term outlook, and may be applicable for locations that have conditions analogous to GDOT’s.

Stability of Repurposed Plastics in Asphalt Binder

Abstract: Five types of waste plastic streams were investigated for their compatibility with a PG 64-22 asphalt binder. The waste plastics included high-density polyethylene (HDPE) pails; low-density polyethylene (LDPE) food packaging waste; polypropylene (PP) from various waste sources, such as plastic cups, medicine bottles, blackboards, and whiteboards; polystyrene (PS) from food packaging; and plastic number 7 from various sources, including polycarbonate, CD cases, and polycarbonate fibers. The plastics were characterized using Fourier-transform infrared spectroscopy (FTIR). Thermogravimetric analysis was performed to optimize the blending. Storage stability of waste plastic–modified binders was evaluated using the cigar tube test (CTT). Rheological and multiple stress creep recovery (MSCR) tests were performed for predicting rutting and cracking potential. No significant chemical changes were noticed when plastics were added to the binder. However, some of the waste plastic–modified binders appeared to be unstable. The rheological properties of waste plastic–modified binder vary with respect to unmodified binder; most plastics exhibited degradation. The MSCR test showed no improvement in the elasticity of the nonrecoverable strain for tested waste plastic–modified binders. This suggests that wet mixing of waste plastic with binder could be ineffective in some cases.

Optimised durable pavement rolling resistance

Abstract: The road-transport sector has a significant impact on energy consumption. A relevant component of this energy usage is associated with rolling resistance (RR) between tires and pavement. In Denmark, CO2 emissions from road transport alone have been quantified as 4.6 Mt/yr. Replacing standard stone asphalt with durable low-RR pavements is expected to reduce CO2 emissions up to 1%. The Danish Road Directorate started, back in 2012, optimising a surface layer for reducing RR. The low-RR mixture was designed to provide a durable texture capable to meet all safety requirements. In 2016, two low-RR mixtures and reference asphalt concrete were paved on a test section. To evaluate the texture durability these mixtures were sampled at the construction site and tested with a circular road tester. The results show that the durability of low-RR pavements can be enhanced by using premodified binder, which reduces changes in texture properties and increases rutting resistance.

Optimized Hot-Mix Asphalt Lift Configuration for Performance

Abstract: Researchers conducted eight large-scale laboratory tests to assess the combined impact of hot-mix asphalt (HMA) overlay mix and thickness on its performance to control reflective cracking. Bonding efficiency, flexibility, and stiffness of the HMA mix as well as overlay thickness significantly affect an overlay’s performance against reflective cracking. Researchers developed a generalized 3D finite-element model to predict an overlay’s reflective cracking potential and generated a database of 128 cases. They also developed a data-driven surrogate model to predict reflective cracking potential that engineers can easily use. Life-cycle cost analysis of overlay alternatives was performed using Illinois Department of Transportation’s unit prices from contracts between 2018 and 2019. The researchers identified optimal overlay configurations to control reflective cracking. An overlay composed of a 1.5 in (38.1 mm) SMA-9.5 or 1.25 in (31.8 mm) IL-9.5FG surface course and a 0.75 in (19.1 mm) IL-4.75 binder course had the lowest annual cost per mile among non-interstate projects. For interstate projects, an overlay composed of a 2 in (50.8 mm) SMA-12.5 surface course and a 2.25 in (57.2 mm) IL-19.0 binder course was the most cost-effective. The study concluded that to control reflective cracking and to reduce life-cycle cost, an overlay composed of an SMA-9.5 surface course and an IL-4.75 binder course is recommended for non-interstate projects. An IL-9.5FG surface course and an IL-4.75 binder course are suggested for low-volume and low-speed roads. For interstate projects, an overlay comprised of an SMA-12.5 surface course and an IL-19.0 binder course is recommended. A data-driven surrogate model may be used to design overlay thicknesses.

Crack Detection of Asphalt Concrete Using Combined Fracture Mechanics and Digital Image Correlation

Abstract: Cracking is a common failure mode in asphalt concrete (AC) pavements. Many tests have been developed to characterize the fracture behavior of AC. Accurate crack detection during testing is crucial to describe AC fracture behavior. This paper proposed a framework to detect surface cracks in AC specimens using two-dimensional digital image correlation (DIC). Two significant drawbacks in previous research in this field were addressed. First, a multi-seed incremental reliability-guided DIC was proposed to solve the decorrelation issue due to large deformation and discontinuities. The method was validated using synthetic deformed images. A correctly implemented analysis could accurately measure strains up to 450\%, even with significant discontinuities (cracks) present in the deformed image. Second, a robust method was developed to detect cracks based on displacement fields. The proposed method uses critical crack tip opening displacement ($\delta_c$) to define the onset of cleavage fracture. The proposed method relies on well-developed fracture mechanics theory. The proposed threshold $\delta_c$ has a physical meaning and can be easily determined from DIC measurement. The method was validated using an extended finite element model. The framework was implemented to measure the crack propagation rate while conducting the Illinois-flexibility index test on two AC mixes. The calculated rates could distinguish mixes based on their cracking potential. The proposed framework could be applied to characterize AC cracking phenomenon, evaluate its fracture properties, assess asphalt mixture testing protocols, and develop theoretical models.

Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network

Abstract: This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised training. CrackPropNet achieved an optimal dataset scale F-1 of 0.755 and optimal image scale F-1 of 0.781 on the testing dataset at a running speed of 26 frame-per-second. Experiments demonstrated that low to medium-level Gaussian noises had a limited impact on the measurement accuracy of CrackPropNet. Moreover, the model showed promising generalization on fundamentally different images. As a crack measurement technique, the CrackPropNet can detect complex crack patterns accurately and efficiently in AC cracking tests. It can be applied to characterize the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.

Flexible Pavement Resiliency and Mitigation Strategies Following Adverse Environmental Events

Abstract: Water accelerates flexible pavement damage in various ways, including subgrade and granular pavement materials strength reduction, frost heave, swelling of expansive subgrade soils, and asphalt binder stripping of aggregate in asphalt concrete (AC) mixes. This paper presents case studies of AC pavement sections in the U.S. that withstood high moisture events with limited effect on pavement performance and no immediate need for major maintenance or rehabilitation actions. The case studies comprise pavement sections in Florida and Louisiana that were subjected to catastrophic flooding events, a pavement section in Delaware that is constantly exposed to tidal water level variations, and pavement sections in Montana and Minnesota that experience seasonal freeze-thaw. These cases illustrated that flexible pavements could withstand climatic changes when appropriately designed and constructed. Mechanically or chemically stabilized subgrade, proper drainage, and/or minimizing plastic fines in unbound layers are all techniques that could effectively control moisture damage.