No abstract

Construction-oriented quantity take-off (QTO) refers to the process of determining the quantities for construction items or work packages in accordance with their descriptions. However, the current construction-oriented QTO practice relies on estimators’ manual interpretation of work descriptions and manual processes to look up proper building objects for quantity calculation. Hence, this research aims to develop natural language processing (NLP) and rule-based algorithms to automate the information extraction (IE) from work descriptions for QTO in building construction. Specifically, several named entity recognition (NER) models, including Hidden Markov Model (HMM), Conditional Random Field (CRF), Bidirectional-Long Short-Term Memory (Bi-LSTM), and Bi-LSTM+CRF, were developed to identify construction activities, material, building component, product features, measurement unit, and additional information (e.g., work scope) from work descriptions. Cost items in the RSMeans database are used to evaluate the developed models in terms of F1 scores. HMM was found to achieve a 5% higher F1 score in the NER than the other three algorithms. Then, labeling rules and active learning strategies were applied along with the HMM model, which improved F1 score by 3% and reduced the labeling efforts by 26%. The results showed that the proposed IE method successfully interprets the desired information from the work description for QTO. This research contributed to the body of knowledge by the NLP-based information extraction model integrating HMM and formalized labeling rules that automatically process work descriptions and lay a foundation for automated QTO and cost estimation.