Assessing the Prevalence of Suspicious Activities in Asphalt
Pavement Construction Using Algorithmic Logics and Machine Learning
Authors: Sakib Mostofa Najmus 
Published: September 2020

Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure
the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of
dollars are invested by government and state highway agencies to construct large-scale HMA
construction projects. QC/QA is statistical approach for checking the desired construction
properties through independent testing. The practice of QC/QA has been encouraged by the
Federal Highway Administration (FHWA) since the mid 60's. However, the standard QC/QA practice
is often criticized on how effective such statistical tests and how representative the reported
material tests are. Material testing data alteration in the HMA construction sector can render
the QC/QA practice ineffective and shadow the performance of asphalt pavements.

The American Society of Civil Engineers estimates that $340 billion is lost globally each
year due to corruption in the construction industry. Asphalt pavement construction consists
of several sectors, including construction and transportation, which are prone to potential
suspicious activities. There is approximately 18 billion tons of asphalt pavement on American
roads, which makes the costs of potential suspicious activities unacceptably large.

The Idaho Transportation Department (ITD) relies on contractor-produced QC test results for
the payment of the HMA pavement projects. In 2017, a case study by FHWA found some unnatural
trends where 74% of the ITD test results didn't match with the contractor results. ITD's approach
to track down the accuracy of mix design and volumetric test data set the off-stage of this
research to mark out instances of suspicious activities in asphalt pavement projects.

The first objective of this research was to develop algorithmic logics to recognize the
patterns of discrepancies in agency- and contractor-produced QC/QA test results. This was
possible with a unique dataset that ITD collected from several dozen HMA projects, in which
all instances of data entry into the material testing report file was recorded in the
background, without the operators' knowledge. My solution was bifurcated into development of
an algorithm combining the logics to automatically detect and categorize suspicious instances
when multiple data entries were observed. Modern data mining approaches were also used to
explore the latent insights and screen out suspicious incidences to identify the chances of
suboptimal materials used for paving and extra payment in HMA pavement projects. I have also
successfully prompted supervised machine learning techniques to detect suspicious cases of
data alterations.

The second step of this research was to calculate the monetary losses due to data alteration.
I replicated ITD's procedure for HMA payment calculation, and quantified payment-related
parameters and associated payment for each project for two cases: 1. when the first parameter
value categorized as Suspicious Alteration (S.A.) was used for payment calculation, and 2.
when the last S.A. parameter value was used for payment. It was evident from my findings that
there has been overpayment on construction projects across Idaho due to material testing data
alterations. Overall, based on the available audit data, I found that overpayments have ranged
from $14,000 to $360,000. Further analysis showed that alteration of each major material
testing parameter's value can cause roughly $1,000 to $5,000 overpayment. I also note that data
alteration did not always cause monetary gains. Other possible motives may include passing
Percent Within Limit (PWL) criteria and precision criteria. Throughout the research, I strive
to automate a suspicious activity detection system and calculate the associated excessive payment.