Rubblization technique has been extensively used to repair the damaged concrete pavement and has proven successful in developed countries like the US and Europe. It has not been fully adopted in developing region like...Rubblization technique has been extensively used to repair the damaged concrete pavement and has proven successful in developed countries like the US and Europe. It has not been fully adopted in developing region like the Middle East and this paper presents the design and construction challenges posed while assessing damaged concrete runway in empty quarter of Saudi Arabia. <span style="font-family:Verdana;">A number of design options for repairs for runway pavement were consi</span><span style="font-family:Verdana;">dered and rubblization was chosen as a preferred option for repair. This paper includes the consideration for the assessment and adoption of the concrete rubblized modulus value using the falling weight deflectometer, optimization </span><span style="font-family:Verdana;">of the tests for the whole runway using the Heavy Weight Deflectometer</span><span style="font-family:Verdana;"> HWD testing to replace pits, safely working around the utilities, reasonable assumption of drop height of the pavement and installation of utility conduits in the rubblized layer. Findings of the paper demonstrates resolving</span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">technical issues which are not very well covered in the Federal Aviation Authority (FAA) EB-66 such as the additional test strips, minimum areas of rubblization for assessment using test pits, drop in the height of concrete surface and fixing of utilities in rubblized pavement. The case study demonstrates that the rubblization can be successfully carried out in remote locations like empty quarter of Saudi Arabia with carefully carried out detailed site investigations, adopt</span><span style="font-family:Verdana;">ing correct assumed design rubblization modulus, quality control using </span><span style="font-family:Verdana;">HWD, protection of utilities while rubblizing and use of polymer modified asphalt for successful project deployment.</span></span></span></span>展开更多
Previous research studies have successfully demonstrated the use of artificial neural network (ANN) models for predicting critical structural responses and layer moduli of highway flexible pavements. The primary objec...Previous research studies have successfully demonstrated the use of artificial neural network (ANN) models for predicting critical structural responses and layer moduli of highway flexible pavements. The primary objective of this study was to develop an ANN-based approach for backcalculation of pavement moduli based on heavy weight deflectometer (HWD) test data, especially in the analysis of airport flexible pavements subjected to new generation aircraft (NGA). Two medium-strength sub-grade flexible test sections, at the National Airport Pavement Test Facility (NAPTF), were modeled using a finite element (FE) based pavement analysis program, which can consider the non-linear stress-dependent behavior of pavement geomaterials. A multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD back-calculation function using the FE program generated synthetic database. At the NAPTF, test sections were subjected to Boeing 777 (B777) trafficking on one lane and Boeing 747 (B747) trafficking on the other lane using a test machine. To monitor the effect of traffic and climatic variations on pavement structural responses, HWD tests were conducted on the trafficked lanes and on the untrafficked centerline of test sections as trafficking progressed. The trained ANN models were successfully applied on the actual HWD test data acquired at the NAPTF to predict the asphalt concrete moduli and non-linear subgrade moduli of the me-dium-strength subgrade flexible test sections.展开更多
文摘Rubblization technique has been extensively used to repair the damaged concrete pavement and has proven successful in developed countries like the US and Europe. It has not been fully adopted in developing region like the Middle East and this paper presents the design and construction challenges posed while assessing damaged concrete runway in empty quarter of Saudi Arabia. <span style="font-family:Verdana;">A number of design options for repairs for runway pavement were consi</span><span style="font-family:Verdana;">dered and rubblization was chosen as a preferred option for repair. This paper includes the consideration for the assessment and adoption of the concrete rubblized modulus value using the falling weight deflectometer, optimization </span><span style="font-family:Verdana;">of the tests for the whole runway using the Heavy Weight Deflectometer</span><span style="font-family:Verdana;"> HWD testing to replace pits, safely working around the utilities, reasonable assumption of drop height of the pavement and installation of utility conduits in the rubblized layer. Findings of the paper demonstrates resolving</span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">technical issues which are not very well covered in the Federal Aviation Authority (FAA) EB-66 such as the additional test strips, minimum areas of rubblization for assessment using test pits, drop in the height of concrete surface and fixing of utilities in rubblized pavement. The case study demonstrates that the rubblization can be successfully carried out in remote locations like empty quarter of Saudi Arabia with carefully carried out detailed site investigations, adopt</span><span style="font-family:Verdana;">ing correct assumed design rubblization modulus, quality control using </span><span style="font-family:Verdana;">HWD, protection of utilities while rubblizing and use of polymer modified asphalt for successful project deployment.</span></span></span></span>
文摘Previous research studies have successfully demonstrated the use of artificial neural network (ANN) models for predicting critical structural responses and layer moduli of highway flexible pavements. The primary objective of this study was to develop an ANN-based approach for backcalculation of pavement moduli based on heavy weight deflectometer (HWD) test data, especially in the analysis of airport flexible pavements subjected to new generation aircraft (NGA). Two medium-strength sub-grade flexible test sections, at the National Airport Pavement Test Facility (NAPTF), were modeled using a finite element (FE) based pavement analysis program, which can consider the non-linear stress-dependent behavior of pavement geomaterials. A multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD back-calculation function using the FE program generated synthetic database. At the NAPTF, test sections were subjected to Boeing 777 (B777) trafficking on one lane and Boeing 747 (B747) trafficking on the other lane using a test machine. To monitor the effect of traffic and climatic variations on pavement structural responses, HWD tests were conducted on the trafficked lanes and on the untrafficked centerline of test sections as trafficking progressed. The trained ANN models were successfully applied on the actual HWD test data acquired at the NAPTF to predict the asphalt concrete moduli and non-linear subgrade moduli of the me-dium-strength subgrade flexible test sections.
