The Impact of Integrating Big Data and Artificial Intelligence Algorithms on Enhancing Time Efficiency Throughout the Life Cycle of Architectural and Urban Planning Projects

Hadi  Bodaghi; Mehdi Mirza Goltabar Roshan; Mohammad Ali  Sayari Nejad

Authors

Hadi Bodaghi Department of Civil Engineering, Sar.C., Islamic Azad University, Sari, Iran Author https://orcid.org/0009-0008-7227-4963
Mehdi Mirza Goltabar Roshan Department of Civil Engineering, Sar.C., Islamic Azad University, Sari, Iran https://orcid.org/0009-0006-0156-8775
Mohammad Ali Sayari Nejad Department of Civil Engineering, QaS.C., Islamic Azad University, Qaemshahr, Iran Author https://orcid.org/0000-0001-7809-0083

Keywords:

Artificial Intelligence, Big Data, Earned Value Management, Critical Path Optimization

Abstract

The contemporary construction and urban development industry faces persistent structural challenges, including substantial schedule deviations, declining productivity, and delays in information feedback, which stem from the rigidity of traditional project management methods and data discontinuities across project life cycle phases. To address these fundamental constraints, the present study investigates the impact of the systematic integration of big data and artificial intelligence (AI) algorithms on enhancing time efficiency throughout the life cycle of architectural and urban planning projects. The research methodology adopts an empirical–analytical approach at two scales, namely complex architectural buildings and large-scale urban infrastructure projects. Accordingly, the effects of implementing intelligent systems were evaluated across four phases: pre-design, planning, construction monitoring, and critical path optimization. The statistical findings demonstrated that integrating generative design based on multi-objective genetic algorithms with predictive solar radiation and climate models reduced the time required to generate and optimize architectural design alternatives by 79.2% compared with conventional parametric methods, decreasing the conceptual decision-making period from 120 hours to 25 hours. During the planning phase, the application of hybrid machine learning models, with an area under the receiver operating characteristic curve exceeding 0.92, filtered out more than 92% of false-positive clashes in Building Information Modeling (BIM), preventing the loss of 1,616 specialist person-hours. Furthermore, during on-site construction, integrating real-time computer vision data streams with Internet of Things (IoT) positioning sensors reduced the reporting delay for construction-site nonconformities from an average of 3.2 working days to 16.2 minutes, representing a 97.2% acceleration in the supervisory feedback cycle. Inferential analyses based on Earned Value Management (EVM) indicators and paired-samples t-tests at the 95% confidence level confirmed that the Schedule Performance Index (SPI) improved to a stable level of 1.04 during the construction phases.

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