As construction noise negatively affects the health and quality of life of stakeholders, field managers need to properly monitor and manage noise. Thus, the authors developed a model that estimates real-time noise levels at a construction site and the surroundings to enable preemptive responses to noise-related issues. To accurately estimate noise, necessary field data were collected using an unmanned aerial vehicle (UAV) and noise sensors. The noise estimation model was composed of two sub-models: the noise-customized spatial interpolation model and the noise propagation model. The noise-customized spatial interpolation model was developed to estimate the internal noise of the construction site using a few sensor noise levels. Meanwhile, the noise propagation model was developed to estimate the noise level outside the construction site using internal noise estimation results, obstacles, weather information, and noise sources information. The model was evaluated through field tests at a construction technology demonstration center, environments identical to real construction sites in South Korea. The model showed satisfactory performance, with an accuracy of 96.71% and a root mean square error (RMSE) of 2.62 for the internal construction site noise and an accuracy of 96.03% and an RMSE of 2.70 for outside the construction site. To facilitate the usage of the noise estimation results for field managers, the research team visualized the results using the Unity 3D Engine. The results will enable field managers to assess workers’ long-term noise exposure and respond to potential civil complaints, gearing up to realize environmental, social, and governance (ESG) goals in the construction industry.

Existing attempts to automate construction document analysis are limited in understanding the varied semantic properties of different documents. Due to the semantic conflicts, the construction specification review process is still conducted manually in practice despite the promising performance of the existing approaches. This research aimed to develop an automated system for reviewing construction specifications by analyzing the different semantic properties using natural language processing techniques. The proposed method analyzed varied semantic properties of 56 different specifications from five different countries in terms of vocabulary, sentence structure, and the organizing styles of provisions. First, the authors developed a semantic thesaurus for construction terms including 208 word-replacement rules based on Word2Vec embedding to understand the different vocabularies. Second, the authors developed a named entity recognition model based on bi-directional long short-term memory with a conditional random field layer, which identified the required keywords from given provisions with an averaged F1 score of 0.928. Third, the authors developed a provision-pairing model based on Doc2Vec embedding, which identified the most relevant provisions with an average accuracy of 84.4%. The web-based prototype demonstrated that the proposed system can facilitate the construction specification review process by reducing the time spent, supplementing the reviewer’s experience, enhancing accuracy, and achieving consistency. The results contribute to risk management in the construction industry, with practitioners being able to review construction specifications thoroughly in spite of tight schedules and few available experts.