...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...

... Publishing Limited Licensed re-use rights only Samad Sepasgozar can be contacted at: dr.samad.sepasgozar@gmail.com Artificial intelligence (AI) Machine learning Large language models Reinforcement learning Deep learning Building information modeling (BIM) Algorithms Architecture Energy...

... at Link to the terms of the CC BY 4.0 licence . Deep learning Structural health monitoring Object detection Piling sheet Visual inspection is widely recognized as one of the most cost-effective non-destructive evaluation (NDE) methods utilized across various industries, including...

... 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. The COVID-19 pandemic has underscored the need of well-planned and maintained HVAC systems in reducing the transmission of airborne diseases...

... 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...

... 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...

...). Figure 3 shows examples of filtered signals. Physiological signal Deep learning Feedforward neural network Accident prediction Unmanned aerial vehicle Construction safety Smart wearable devices have facilitated the collection of vital human signs such as heart rate (HR), heart rate...

..., these methods cannot quickly provide data for the inspection of heritage structures. Due to such limitations, the primary inspection of the heritage structure takes more time and cannot be done frequently. CNN is the primary deep learning method that can automatically classify the images based on training...