Dec. 2, 2020
Two MSU civil engineering PhD students take national honors
Pavement performance research by two MSU civil engineering PhD students received national recognition during the Long-Term Pavement Performance Analysis Student Contest - a joint effort of the Federal Highway Administration (FHWA) and the American Society of Civil Engineers.
Hamad Bin Muslim won first place honors for his paper presentation in the graduate student category. The top honor qualifies him to receive complimentary conference access to the Transportation Research Board's 100th annual meeting, which will be hosted virtually throughout the month of January.
He also won $1,000 and his winning paper will be published by FHWA and made available to researchers worldwide.
Muhammad Munum Masud won second place in the Aramis Lopez Challenge category. He will receive $1,000 and certificate recognizing his accomplishment.
Both PhD students are members of MSU’s GEO-PAVE group and are advised by Associate Professor Syed Waqar Haider in the Department of Civil and Environmental Engineering.
Read more on the award-winning papers:
Transportation & Development Institute of the American Society of Civil Engineers (T&DI/ASCE) and the Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) Program Award
• “Effects of Seasonal And Diurnal Fwd Measurements on LTE Of JPCP – LTPP SMP Data.”
By: Hamad Bin Muslim and Syed Waqar Haider
Non-destructive Falling Weight Deflector (FWD) deflection measurements are used to characterize the structural capacity of pavements. Load transfer efficiency (LTE) is one of the critical pavement condition parameters determined based on FWD deflections measured on rigid pavement joints and cracks. However, diurnal and seasonal temperature variations significantly influence the FWD deflections.
Since the slab curling due to temperature and moisture gradients affect the measured deflections, there is a need to develop guidelines for the deflection measurements to estimate pavement conditions accurately. The Seasonal Monitoring Program (SMP) study in the Long-term Pavement Performance (LTPP) database is a unique resource that has FWD measurements conducted in multiple seasons and diurnally along with temperature data (air, surface, and gradient) to analyze such effects.
LTE data from the LTPP SMP database for the available JPCP sections were analyzed to investigate the effects of diurnal and seasonal FWD measurements. The analysis showed that the season and time of FWD measurement have a significant influence on the pavement deflections and corresponding calculated parameters such as LTE. Results showed that FWD measurements should be avoided on rigid pavements once ambient temperatures are in access of 75ᵒF or below 35ᵒF. Such temperatures lead to over- or under-estimation of joint load transfer efficiency. Also, before-noon measurements were found beneficial for accurately assessing the actual pavement condition.
• “Weigh-in-Motion Accuracy Prediction using Axle Load Spectra and Effect of Overloading Vehicles on Pavement Performance.”
By: Muhammad Munum Masud and Syed Waqar Haider
Highway agencies collect Weigh-in-Motion (WIM) data for many reasons, including highway planning, pavement and bridge design, freight movement studies, motor vehicle enforcement, and regulatory studies. The new mechanistic-empirical pavement design guide (Pavement-ME) also requires WIM data for predicting pavement distresses. Inappropriate WIM data may result in significant over- or underestimation of the pavement performance period and hence, lead to premature failure. Therefore, the data collected at WIM systems must be accurate and consistent.
Since a significant amount of WIM accuracy and axle load spectra data are available in the LTPP traffic module, it is essential to review the data extents and its potential application to improve the WIM accuracy and pavement performance. This paper addresses two main issues related to traffic loadings; (1) how to obtain accurate and reliable WIM accuracy data, and (2) how overloaded trucks impact the expected pavement performance. The primary objectives of the paper are to provide (a) review of high-quality LTPP WIM data, (b) WIM accuracy relationship with NALS shape factors, (c) statistical analysis to develop a predictive model for WIM accuracy, (d) distribution of overloaded trucks using NALS data, and (e) effect of overloading on pavement performance using the Pavement-ME. These objectives were accomplished by synthesizing and analyzing the WIM data available in the LTPP database.
The data review revealed that the majority of the WIM sites have bending plate (BP) sensors followed by quartz piezo (QP), and load cell (LC) sensors in the LTPP high-quality WIM database. Also, the majority of the WIM sites are located in a wet climate. The statistical analysis was performed using principal component analysis (PCA), classification and regression trees (CART), simple linear, multiple linear, and binary logistic regression. The data analysis shows that the WIM accuracy for the tandem axle (TA) can be estimated with TA NALS shape factors with an acceptable degree of error. The results also show that 6 percent positive bias, and 8 percent negative bias for TA in load spectra resulted in significant over and underprediction of predicted rutting and fatigue performance. At the end of design life at 20 years (@90% reliability), the positive and negative bias resulted in over and underprediction of bottom-up cracking by 42% and 15%, respectively. For the total permanent deformation, the positive and negative bias resulted in 5%, and 2.3% over, and under predictions, respectively.