...Aleksander Gil; Yusuf Arayici Purpose This paper presents an advanced deep learning methodology to overcome critical limitations in machine learning and computer vision approaches for heritage building information modelling (HBIM) point cloud classification, including the inability to handle fixed...

... adoption framework of PERGE is established, offering valuable insights for researchers and practitioners along with the identification of key trends, suitable AI methods and underexplored opportunities. (Title-Abs-Key (“artificial intelligence” OR “machine learning” OR “deep learning” OR “generative AI...

... of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at Link to the terms of the CC BY 4.0 licence . Deep learning Structural health monitoring Object detection Piling sheet...

... reduce energy consumption and enhance overall efficiency. Design/methodology/approach The research utilizes deep learning models to address variables in building design, an area that previous studies have not fully explored. A dataset from arid climate regions was used to train and test two deep...

.... It illustrates how deep learning may increase the precision of fault identification and computational efficiency in medical settings by utilizing LSTM and GRU models. Fault detection diagnostics Hospital HVAC system Energy Deep learning BMS Global warming has contributed to higher temperatures...

... and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks. Design/methodology/approach Three long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely...

...Fatemeh Mostafavi; Mohammad Tahsildoost; Zahra Sadat Zomorodian; Seyed Shayan Shahrestani Purpose In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process...

...Mergen Kor; Ibrahim Yitmen; Sepehr Alizadehsalehi Purpose The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis. Design/methodology/approach...

... performance on real-world images. Originality/value This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust. Pingbo...

... Wearable sensors Augmented reality Deep learning Long short-term memory The construction industry, being one of the industries with the largest labor force in the United States [8% of the total workforce (BLS, 2018)], continuously struggles with non-fatal injuries associated with musculoskeletal...

... studies regardless of the choice of lower and upper bound values. Physiological signal Deep learning Feedforward neural network Accident prediction Unmanned aerial vehicle Construction safety As shown in Plate 1 , an outdoor courtyard and an immersive VR training replica were used...

... 2020 20 11 2020 20 12 2020 © Emerald Publishing Limited 2020 Emerald Publishing Limited Licensed re-use rights only Visual inspection Automatic defect detection Deep learning Automatic inspection system Artificial intelligence Heritage is an invaluable part...