Abstracts – Browse Results
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Abdeen, F N, Gunatilaka, R N, Sepasgozar, S M E and Edwards, D J (2024) The usability of a novel mobile augmented reality application for excavation process considering safety and productivity in construction. Construction Innovation, 24(04), 892-911.
Ahmadian, F F A, Rashidi, T H, Akbarnezhad, A and Waller, S T (2017) BIM-enabled sustainability assessment of material supply decisions. Engineering, Construction and Architectural Management, 24(04), 668-95.
Al-Hammad, A M, Al-Mubaiyadh, S and Mahmoud, T (1996) Public versus private sector's assessment of problems facing the building maintenance industry in Saudi Arabia. Building Research & Information, 24(04), 245–54.
Bajpai, A and Misra, S C (2024) Evaluation of success factors to implement digitalization in the construction industry. Construction Innovation, 24(04), 865-91.
Best, R and Langston, C (2006) Evaluation of construction contractor performance: a critical analysis of some recent research. Construction Management and Economics, 24(04), 439-45.
Buijs, A and Silvester, S (1996) Demonstration projects and sustainable housing. Building Research & Information, 24(04), 195–202.
Cheung, F K T and Skitmore, M (2006) A modified storey enclosure model. Construction Management and Economics, 24(04), 391-405.
Daniel, E I, Oshodi, O, Dabara, D and Dimka, N (2024) Towards closing the housing gap in the UK: exploration of the influencing factors and the way forward. Construction Innovation, 24(04), 965-85.
Do, S T, Nguyen, V T and Banlasan, D (2024) Social media sensing framework for urban infrastructure management: a Philippine case study. Construction Innovation, 24(04), 1117-36.
Ejohwomu, O A, Oshodi, O S and Lam, K C (2017) Nigeria’s construction industry: Barriers to effective communication. Engineering, Construction and Architectural Management, 24(04), 652-67.
Ekanayake, B, Ahmadian Fard Fini, A, Wong, J K W and Smith, P (2024) A deep learning-based approach to facilitate the as-built state recognition of indoor construction works. Construction Innovation, 24(04), 933-49.
- Type: Journal Article
- Keywords: as-built state; deep learning; google colab; indoor construction progress monitoring; virtual machine; yolov4
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-05-2022-0121
- Abstract:
Purpose: Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works. Design/methodology/approach: The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images. Findings: The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images. Originality/value: This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised. © 2022, Emerald Publishing Limited.
Enshassi, A (1996) Training for Palestinian engineers to face the challenges of multinational enterprises in the Gaza Strip. Building Research & Information, 24(04), 222–7.
GhaffarianHoseini, A, Tien, D D, Naismith, N, Tookey, J and GhaffarianHoseini, A (2017) Amplifying the practicality of contemporary building information modelling implementations for New Zealand green building certification (green star). Engineering, Construction and Architectural Management, 24(04), 696-714.
Golabchi, H and Hammad, A (2024) Estimating labor resource requirements in construction projects using machine learning. Construction Innovation, 24(04), 1048-65.
Gunning, J G and Cooke, E (1996) The influence of occupational stress on construction professionals. Building Research & Information, 24(04), 213–22.
Hassanein, A A G (1996) Factors affecting the bidding behaviour of contractors in Egypt. Building Research & Information, 24(04), 228–36.
Hassanein, A A G and Hakam, Z H R (1996) A bidding decision index for construction contractors. Building Research & Information, 24(04), 237–44.
Hu, X and Liu, C (2017) Total factor productivity measurement with carbon reduction. Engineering, Construction and Architectural Management, 24(04), 575-92.
Huang, Y-L and Chou, S-P (2006) Valuation of the minimum revenue guarantee and the option to abandon in BOT infrastructure projects. Construction Management and Economics, 24(04), 379-89.
Johnson, C, Lizarralde, G and Davidson, C H (2006) A systems view of temporary housing projects in post-disaster reconstruction. Construction Management and Economics, 24(04), 367-78.
Kazemi, R M, Riley, D, Asadi, S and Delgoshaei, P (2017) Improving the performance profile of energy conservation measures at the Penn State University Park campus. Engineering, Construction and Architectural Management, 24(04), 610-28.
Kindangen, J I (1996) Artificial neural networks and naturally ventilated buildings. Building Research & Information, 24(04), 203–8.
Malla, V (2024) Structuration of lean-agile integrated factors for construction projects. Construction Innovation, 24(04), 986-1004.
Oke, A E, Aliu, J, Onajite, S and Simeon, M (2024) Success factors of digital technologies (DT) tools adoption for sustainable construction in a developing economy. Construction Innovation, 24(04), 950-64.
Oke, A E, Aliu, J, Tunji-Olayeni, P and Abayomi, T (2024) Application of gamification for sustainable construction: an evaluation of the challenges. Construction Innovation, 24(04), 1066-84.
Olugboyega, O, Ilesanmi, K E, Oseghale, G E and Aigbavboa, C (2024) The link between construction apps’ acceptance and digital attributes of construction professionals: perspectives from digital competence model. Construction Innovation, 24(04), 912-32.
Panahi, B, Moezzi, E, Preece, C N and Wan, Z W N (2017) Value conflicts and organizational commitment of internal construction stakeholders. Engineering, Construction and Architectural Management, 24(04), 554-74.
Parchami, J M and Shoar, S (2017) A hybrid SD-dematel approach to develop a delay model for construction projects. Engineering, Construction and Architectural Management, 24(04), 629-51.
Rahmani, F, Maqsood, T and Khalfan, M (2017) An overview of construction procurement methods in Australia. Engineering, Construction and Architectural Management, 24(04), 593-609.
Silveira, B F and Costa, D B (2024) Method for automating the processes of generating and using 4D BIM models integrated with location-based planning and Last Planner® System. Construction Innovation, 24(04), 1005-25.
Stewart, R A and Spencer, C A (2006) Six-sigma as a strategy for process improvement on construction projects: a case study. Construction Management and Economics, 24(04), 339-48.
Tang, C M, Wong, C W Y, Leung, A Y T and Lam, K C (2006) Selection of funding schemes by a borrowing decision model: a Hong Kong case study. Construction Management and Economics, 24(04), 349-65.
Thomas, A V, Kalidindi, S N and Ganesh, L S (2006) Modelling and assessment of critical risks in BOT road projects. Construction Management and Economics, 24(04), 407-24.
Uzun, C and Cangür, R E (2024) An ontological assessment proposal for architectural outputs of generative adversarial network. Construction Innovation, 24(04), 1165-84.
Wang, K, Guo, F, Zhou, R and Qian, L (2024) Implementation of augmented reality in BIM-enabled construction projects: a bibliometric literature review and a case study from China. Construction Innovation, 24(04), 1085-116.
Wilson, J G and Gupta, N K (1996) Assessment of structure formation in fresh concrete by measurement of its electrical resistance. Building Research & Information, 24(04), 209–12.
Wong, J T Y and Hui, E C M (2006) Construction project risks: further considerations for constructors' pricing in Hong Kong. Construction Management and Economics, 24(04), 425-38.
Wuni, I Y and Mazher, K M (2024) Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis. Construction Innovation, 24(04), 1026-47.
Zoleykani, M J, Abbasianjahromi, H, Banihashemi, S, Tabadkani, S A and Hajirasouli, A (2024) Extended reality (XR) technologies in the construction safety: systematic review and analysis. Construction Innovation, 24(04), 1137-64.