Spring 2022 Kent Seminar Series presents Qingqing Cao, Xiuyu Liu
By Qingqing Cao and Xiuyu Liu on 1/20/2022 from 2 p.m. to 3 p.m. in 1611 Titan Drive Rantoul, IL 61866
Join Qingqing Cao and Xiuyu Liu, doctoral candidates at the University of Illinois Urbana-Champaign's Department of Civil and Environmental Engineering, as they present in-person at the Spring 2022 Kent Seminar Series Thursday, Jan. 20, from 2-3 p.m. (CT).
Pizza and soft drinks will be provided beginning at 1:30 p.m. in the ICT Classroom.
All presentations will be held on Zoom, but some speakers will present in person at ICT. Find more information on tomorrow's presentations below.
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Presenter: Qingqing Cao, doctoral candidate
Presentation title: Development of a simulation-based approach for cold in-place recycled pavement moisture-content prediction using ground-penetrating radar
Abstract: Ground-penetrating radar recently has been used for quality control and quality assurance of the asphalt concrete pavement-construction process. The objective of this study was to investigate the feasibility of estimating, by using GPR, the moisture content in AC pavement. This application is particularly important for emulsion-stabilized cold in-place recycling (CIR) and cold central-plant recycling, where monitoring the moisture content is necessary for deciding the timing of opening the road to traffic and/or overlay placement. Four field tests were performed using GPR on CIR- or CCPR-treated AC pavement. A numerical simulation model of AC pavement with internal moisture was generated using the information from mix design, and virtual GPR tests were performed using the finite-difference time-domain method. After calibration, a moisture-prediction formula derived from the simulation model was used to correlate the dielectric constant predicted by GPR to the moisture content within cold recycled layers. The GPR signal was denoised by improving its stability and mitigating the measured-height mismatch. The in-situ moisture content was predicted using the proposed method and compared with field-collected samples. Results showed that the proposed method is effective in estimating CIR- and CCPR-layer moisture content. The variation of dielectric constants in field tests is also discussed. A testing protocol for predicting moisture content using GPR is suggested for CIR and CCPR pavement.
Bio: Qingqing Cao is a Ph.D. candidate at UIUC. She received the B.Sc. and M.Sc. degrees from Southeast University, Nanjing, China, in 2015 and 2018, respectively. Her research interests include application of machine learning in pavement engineering, development of advanced digital signal processing techniques for radar signal, and simulation of ground penetrating radar signal in asphalt pavement.
Presenter: Xiuyu Liu, doctoral candidate
Presentation title: An integrated vehicle–tire–pavement approach for determining pavement structure–induced rolling resistance under dynamic loading
Abstract: Pavement-related rolling resistances, caused by pavement–vehicle interaction, are important components of pavement life-cycle assessment . Structure-induced rolling resistance is caused by dissipated vehicle kinetic energy in the pavement structure. This paper presents an integrated vehicle–tire–pavement approach to evaluate the SRR of asphalt pavement under dynamic loading. A 3D semitrailer-truck model was used to calculate dynamic wheel loads on various pavement surface-roughness profiles. The dynamic wheel loads were then transformed into 3D tire–pavement contact stresses using a deep-learning tire model. Next, an advanced 3D finite-element pavement model, validated in previous studies, was utilized to simulate pavement structure under moving tire–pavement contact stresses. It was found that the dynamic load coefficient of axle forces increased linearly with truck speed. The increasing trend became more significant as pavement roughness increased. Ignoring the dynamic loading effect resulted in 12% error in predicting SRR. A case study was performed to illustrate the computation procedures of asphalt-pavement SRR under static and dynamic loading. The dynamic loading accounted for 4.74%, 7.37%, 10.73%, and 14.02% of SRR for four pavement surface-roughness levels at a truck speed of 40 mph. In addition, the SRR was found to be highly nonlinear and increased as speed decreased and axle load increased. A modified Illinois Center for Transportation SRR model was developed to provide a quick assessment of the SRR component of the pavement LCA use stage. This study demonstrated the importance of vehicle–tire–pavement interaction in SRR prediction, which may not be overlooked.
Bio: Xiuyu Liu is a Ph.D. candidate at UIUC. He received the B.Sc. and M.Sc. degrees from Southeast University, Nanjing, China, in 2015 and 2018, respectively. His research interests include application of vehicle dynamics in pavement engineering, development of machine learning models for computational mechanics, and numerical simulation of vehicle-pavement interaction.