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A large proportion of construction accidents are caused by workers' unsafe actions. Due to the complexity of the work environment and excessive demands of safety supervision on construction sites, many modern information technologies, such as computer vision (CV) technologies, have been increasingly applied and gradually replaced the traditional manual supervision to automatically identify unsafe behaviors from surveillance video data. Current models focus on high resolution (HR) video containing high quality features. However, challenges remain for more specific real-world construction scenarios, including but not limited to lack of high-quality data, far-field video recognition, privacy protection, and limited computing resources. To address the challenges, in this paper, a simple but effective method relying on the Teacher–Student Learning Architecture is proposed, where the teacher network assists the student network in effectively capturing useful features for downstream action recognition from extreme low resolution (eLR) video which can be easily transferred to other tasks. In addition, a Knowledge Learning Model and a similarity loss function are proposed to better guide the training of student network. In numerical experiments and case studies, our proposed training strategy achieves an top-1 accuracy of 28.53% and 71.81% on eLR HMDB dataset and eLR CMA dataset, 5.96% and 3.89% higher than the baseline model. The proposed strategy can make the automatic safety monitoring of construction works more accurate and reliable, thereby effectively reducing the accident rate and management cost. In addition, the performance of proposed strategy depends on efficient knowledge distillation methods, which may inspire future applications of CV-based deep learning models in construction management to achieve higher recognition results while maintaining low computational cost. [ABSTRACT FROM AUTHOR]

A large proportion of construction accidents are caused by unintentional and unsafe actions and behaviors. It is of significant difficulties and ineffectiveness to monitor unsafe behaviors using conventional manual supervision due to the complex and dynamic working conditions on construction sites. Recently, surveillance videos and computer vision (CV) techniques have been increasingly adopted to automatically identify risky behaviors. However, the challenge remains that spatial and temporal features in video clips cannot be effectively captured and fused by current CV models. To address this challenge, this paper describes a deep learning model named Spatial Temporal Relation Transformer (STR-Transformer), where spatial and temporal features of work behaviors are simultaneously extracted in paralleling video streams and then fused by a specially designed module. To verify the effectiveness of the STR-Transformer, a customized dataset is developed, including seven categories of construction worker behaviors and 1595 video clips. In numerical experiments and case studies, the STR-Transformer achieves an average precision of 88.7%, 4.0% higher than the baseline model. The STR-Transformer enables more accurate and reliable automatic safety surveillance on construction projects, and is expected to reduce accident rates and management costs. Moreover, the performance of STR-Transformer relies on efficient feature integration, which may inspire future studies to identify, extract, and fuse richer features when applying CV-based deep learning models in construction management. • A deep learning model is proposed for action recognition. • The model consists of two feature encoders and a feature fusion model. • The model identify unsafe actions of construction workers. • A novel dataset of construction worker actions is proposed. [ABSTRACT FROM AUTHOR]

Unstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements, this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques. • An in-depth review of natural language processing (NLP) for smart construction is conducted. • 124 journal articles published in 2000–2021 are manually reviewed. • Bottom-level NLP techniques are thoroughly introduced. • NLP is used to extract and exchange information and to support downstream applications. • The role of NLP in smart construction, current challenges, and future directions are provided. [ABSTRACT FROM AUTHOR]

Adaptive work packaging is paramount in helping reduce dynamic gaps between design and manufacturing in modular construction (MC), particularly in mass customization. However, current work packaging methods fail to automatically extract complex semantic relations among work package elements (e.g., products, tasks, and their dependencies) and dynamically reason the implicit semantic knowledge (e.g., the different granularity of semantics) as the project progresses. To address these issues, this study proposes a knowledge graph-enabled adaptive work packaging (K-GAWP) approach to dynamically form semantic-enriched work packages with different granularities. Thus far, this study first models the data of tasks, products, and their spatial relationships for MC production as graphs. Second, a novel multi-granularity knowledge reasoning method (product2task) is developed to map products to tasks in an adaptive manner. Third, a dedicated hierarchical clustering method (task2package) involving multiple features from the dependency structure matrix is proposed for work-package generation (i.e., task knowledge fusion). Finally, the K-GAWP's performance is evaluated through controlled experiments in a real MC project. The results indicate that the K-GAWP approach performs work packaging in an adaptive, accurate, and efficient manner, thereby improving the distributed planning and control of MC projects. [ABSTRACT FROM AUTHOR]