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.
- Type: Journal Article
- Keywords: additive manufacturing; building construction technology; deep reinforcement learning; large-scale on-site application; tower crane
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-10-2022-0278
- Abstract:
Purpose: Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process. Design/methodology/approach: The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance. Findings: The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization. Originality/value: In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent. Practical implications: The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres. © 2022, Emerald Publishing Limited.
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.
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.