Abstracts – Browse Results
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Doukari, O, Kassem, M, Scoditti, E, Aguejdad, R and Greenwood, D (2024) A BIM based tool for evaluating building renovation strategies: the case of three demonstration sites in different European countries. Construction Innovation, 24(01), 365-83.
Faraji, A, Homayoon Arya, S, Ghasemi, E, Rashidi, M, Perera, S, Tam, V and Rahnamayiezekavat, P (2024) A conceptual framework of decentralized blockchain integrated system based on building information modeling to steering digital administration of disputes in the IPD contracts. Construction Innovation, 24(01), 384-406.
Garip, S B, Güzelci, O Z, Garip, E and Kocabay, S (2024) A genetic algorithm-based design model to provide reduced risk areas for housing interiors. Construction Innovation, 24(01), 49-66.
Gledson, B, Zulu, S L, Saad, A M and Ponton, H (2024) Digital leadership framework to support firm-level digital transformations for Construction 4.0. Construction Innovation, 24(01), 341-64.
Jowett, B, Edwards, D J and Kassem, M (2024) Field BIM and mobile BIM technologies: a requirements taxonomy and its interactions with construction management functions. Construction Innovation, 24(01), 134-63.
Likita, A J, Jelodar, M B, Vishnupriya, V and Rotimi, J O B (2024) Lean and BIM integration benefits construction management practices in New Zealand. Construction Innovation, 24(01), 106-33.
Lisco, M and Aulin, R (2024) Taxonomy supporting design strategies for reuse of building parts in timber-based construction. Construction Innovation, 24(01), 221-41.
Mahamedi, E, Wonders, M, Gerami Seresht, N, Woo, W L and Kassem, M (2024) A reinforcing transfer learning approach to predict buildings energy performance. Construction Innovation, 24(01), 242-55.
Matoseiro Dinis, F, Rodrigues, R and Pedro da Silva Poças Martins, J (2024) Development and validation of natural user interfaces for semantic enrichment of BIM models using open formats. Construction Innovation, 24(01), 196-220.
Parisi, F, Sangiorgio, V, Parisi, N, Mangini, A M, Fanti, M P and Adam, J M (2024) A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning. Construction Innovation, 24(01), 8-32.
Philip, B and AlJassmi, H (2024) Time-series forecasting of road distress parameters using dynamic Bayesian belief networks. Construction Innovation, 24(01), 317-40.
Rampini, L and Re Cecconi, F (2024) Synthetic images generation for semantic understanding in facility management. Construction Innovation, 24(01), 33-48.
Saif, W and Alshibani, A (2024) A close-range photogrammetric model for tracking and performance-based forecasting earthmoving operations. Construction Innovation, 24(01), 164-95.
- Type: Journal Article
- Keywords: 3D data capturing; close-range photogrammetry (crp); construction 4.0; cost and schedule forecasting; delaunay triangulation; digital camera; earthmoving operations; point cloud; productivity monitoring; smartphone
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-12-2022-0323
- Abstract:
Purpose: This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models. Design/methodology/approach: The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project. Findings: The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data. Originality/value: The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp). © 2023, Emerald Publishing Limited.
Sati, A and Al-Tabtabai, H (2024) A paradigm shift toward the application of blockchain in enhancing quality information management. Construction Innovation, 24(01), 407-24.
Singh, A, Kumar, V, Mittal, A and Verma, P (2024) Identifying critical challenges to lean construction adoption. Construction Innovation, 24(01), 67-105.
Yu, J, Zhong, H and Bolpagni, M (2024) Integrating blockchain with building information modelling (BIM): a systematic review based on a sociotechnical system perspective. Construction Innovation, 24(01), 280-316.
Zani, A, Speroni, A, Mainini, A G, Zinzi, M, Caldas, L and Poli, T (2024) Customized shading solutions for complex building façades: the potential of an innovative cement-textile composite material through a performance-based generative design. Construction Innovation, 24(01), 256-79.