Representing Collected Road Condition Data with Chernoff Faces for Evaluation of Pavement Conditions

  • Aioub H. GuhaEmail author
  • Gabriel J. Assaf
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)


Maintenance engineers have a problem because of the great quantity of data they must consider when evaluating kilometres of roads and because road roughness data measured with IRI (or possibly PCI) cannot be considered in isolation from other data such as Construction and Workmanship; Mix and Structural Design; Traffic; Climate (during and after construction). Incorporating these five datapoints into a single conventional graph would require a graph in five dimensions and such a graph would lose much of the benefits that a graph is meant to give (i.e. speed of data comprehension and readability). The Chernoff Face Method (CFM) is an iconic visualization technique used to code multivariate data to simplify two-dimensional line drawings of faces so as to create a representation of the data that is more intuitively comprehensible to humans. The theory behind CFM is that humans have a special sensitivity to details in a facial representation that they do not have with data representations such as conventional graphic displays (e.g. line and bar graphs). The objective is to find an optimum balance between the volume of the data represented and the comprehensibility of the data. In particular, humans can discern eye size and eyebrows slant in faces with finer distinctions than they would be able to intuitively understand when reading another graph. This promises to be a means of displaying fine grained data for analysis by mapping data points to possible display variables such as: eye size, face shape, eyebrows slant angle (inward, outward, neutral). In order to map the IRI, construction, design, traffic and climate data to the CFM, a Multi-Criteria Analysis (MCA) model was used to determine the optimal number for the scale for each data category. The total number of Chernoff Faces for this problem was found to be 243, so that all five categories of data could be represented. Using CFM in conjunction with this data allows better evaluation of pavement distress.


Chernoff face International roughness index Traffic level Climate zone Structural design Material & construction Multi-criteria analysis 


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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering & ConstructionÉcole de Technologie Supérieure (ÉTS), University of QuébecMontréalCanada
  2. 2.Department of ConstructionTechnical Faculty of Structural EngineeringMeslataLibya

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