2026-09-19T22:48:13Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/29519 2023-07-10 col_2142_14770 col_2142_5131 com_2142_3518 com_2142_234 com_2142_5130

Tutumluer, Erol Ghaboussi, Jamshid Tutumluer, Erol Ghaboussi, Jamshid Thompson, Marshall R. Al-Qadi, Imad L. Pekcan, Onur 2012-02-01T00:53:42Z 2012-02-01T00:53:42Z 2014-02-01T11:00:33Z 2011-12 2012-02-01T00:53:42Z 2011-12 Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-07-14T19:09:40Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Pekcan_Onur.pdf: 54856346 bytes, checksum: bb550be9dcefd4a09f02c28dfe3c887b (MD5) Made available in DSpace on 2012-02-01T00:53:42Z (GMT). No. of bitstreams: 2 Pekcan_Onur.pdf: 54856346 bytes, checksum: bb550be9dcefd4a09f02c28dfe3c887b (MD5) license.txt: 4071 bytes, checksum: 8c345f8d185f8df24ed252061a2ce1eb (MD5) Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram (wingram2@illinois.edu) on 2012-02-01T00:57:02Z Item is restricted until 2014-02-01T00:56:58Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2014-02-01T11:00:33Z Item was in collections: Dissertations and Theses - Civil and Environmental Engineering (ID: 672) Graduate Theses and Dissertations at Illinois (ID: 204) No. of bitstreams: 3 Pekcan_Onur.pdf.txt: 309015 bytes, checksum: 72a87584df84a84f672cdf0b4fbacab6 (MD5) Pekcan_Onur.pdf: 54856346 bytes, checksum: bb550be9dcefd4a09f02c28dfe3c887b (MD5) license.txt: 4071 bytes, checksum: 8c345f8d185f8df24ed252061a2ce1eb (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2014-02-01T11:00:33Z http://hdl.handle.net/2142/29519 en Copyright 2011 Onur Pekcan transportation geotechnics pavements geomechanical systems inverse problems Parameter identification backcalculation falling weight deflectometer artificial neural networks genetic algorithms soft computing nondestructive testing asphalt concrete lime stabilization subgrade SOFTSYS finite element method nonlinear finite element method ILLI-PAVE Soft computing based parameter identification in pavements and geomechanical systems Civil & Environmental Eng 1251 Civil Engineering 0106 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Civil Engineering -UIUC 10KS0106PHD