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.
- Type: Journal Article
- Keywords: dynamic Bayesian belief networks; performance prediction modeling; road distress parameters; time-series data
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-09-2022-0233
- Abstract:
Purpose: To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters. Design/methodology/approach: While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors. Findings: The four developed performance prediction models achieved an overall precision and reliability rate of over 80%. Originality/value: The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized. © 2023, Babitha Philip and Hamad AlJassmi.
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.