Comparing Two Artificial Intelligence Language Modeling to Evaluate Construction Schedule Understanding

Abstract

Construction schedules are critical for construction projects. Creating a construction schedule is complex. Because it requires a keen ability to understand construction documents and methods. Furthermore, a single construction project could be completed following multiple construction schedules. The preparation of construction schedules is currently done by humans. However, with the advent of Artificial Intelligence (AI), it is possible to think of a future where humans could be assisted by an AI to develop construction schedules. An important step toward having an AI assisting humans in the creation of construction schedules is for the AI to understand construction schedules. Thus, this paper presents the comparison of two language modeling to evaluate an AI's ability to understand construction schedules. This comparison was done following a quantitative experimental research methodology where the independent variables were two AI language models (the Bidirectional Encoder Representation Transformers and the Masked and Permuted Pre-training for Language Understanding) and the dependent variables were accuracy, precision, recall, and F1 scores of the language modeling to understand construction schedule activities. The results demonstrate the impact of language models on the ability of the AI to understand construction schedules. The Masked and Permuted Pre-training for Language Understanding language model had an overall superior performance in understanding construction scheduling activities. This is important as supports the need to expand research projects as the one presented in his paper to identify the best language modeling and improve it for the construction industry.