Abstract:
The construction sector is leading in the number of accidents caused by the unsafe
behavior of workers. Unsafe behavior can arise from a worker’s personal preferences
or from any external unsafe condition in his working environment. Personal preferences
are the intentional unsafe practices of workers and various behavioral constructs drive
these preferences. On the other hand, the absence of no measures and controls for
unsafe behaviors at the organizational level can be treated as an external unsafe
condition that leads to unsafe acts. Therefore, this study proposes a propensity
prediction engine powered by the classification algorithm of Artificial neural networks
(ANN). The ANN-based propensity model takes quantified values of individual
features of behavior-modifying constructs as inputs and provides outputs in the form of
classification of workers as safe or unsafe. The behavioral constructs are taken from the
theory of planned behavior (TPB) and the individual features of these constructs are
explored from previous studies and field surveys. The model is a multi-layer feed-
forward network with back-propagation built on an architecture of 10-16-6-2 and has
been trained, validated, and tested using Keras API of Tensorflow. The study also
presents a framework for practical implementations of propensity prediction engine for
construction organizations. Specialized behavior interventions are proposed to be
included in safety training programs for worker’s classified as unsafe by the prediction
engine. The engine will help construction organizations in improving their safety
training program by providing a way of managing behabehavioragement gaps at the
organizational level.
