Despite significant advancements, the productivity rate in the construction industry has only increased by 1% over the last 20 years. In contrast, other industries, such as the automobile sector, have seen productivity improvements of up to 3%.The construction sector employs 26% of the total workforce both locally and internationally, and 16.3% of total registered local SMEs are construction-related.
However, the main question remains: how can we boost productivity rates? The answer lies in utilising digital twins and artificial intelligence (AI) to streamline processes and minimise human involvement in certain tasks. Dr. Mansour AlOtaibi, Chairman of the Architectural Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), discusses this and provides a comprehensive overview of the construction sector in Saudi Arabia.
Utilising Digital Twins for Project Efficiency
Digital twins offer a revolutionary approach to managing construction projects, particularly in terms of scheduling and cost monitoring. Research shows that employing digital twin technology can lead to a 28% improvement in project scheduling and a significant reduction in cost overruns. Traditional practices often result in projects being 53% behind schedule and 66% over budget. The key challenges include inconsistent progress reporting, inadequate communication between parties, and infrequent performance management.
To address these issues, it’s essential to implement platforms that monitor daily progress on job sites and ensure short-term planning aligns with actual progress. A close examination of performance variations reveals that over 80% are related to changes in work, underscoring the importance of integrating virtual design during the planning stage to minimise clashes prior to execution.
Addressing Project Risks with Predictive Models
One of the most significant pain points in construction project management is the insufficient handling of project risks. Many involved in projects lack access to actionable platforms and wearable systems that can predict potential risks and provide recommendations ahead of schedule. Dr. AlOtaibi’s research group focuses on developing models that anticipate and mitigate these risks, particularly in unique and complex renovation projects.
Renovation projects pose their own set of challenges, often involving unforeseen conditions that can disrupt plans. By modelling proactive approaches, it is possible to minimise the impact of these unforeseen conditions. This approach is crucial for upgrading infrastructure and ensuring that renovation projects do not negatively affect rental income for property owners.
Real-World Applications: Case Study on Renovation Projects
A practical case study highlights the application of AI and digital twin technology in a renovation project involving 17 lease units. The challenge was to determine the optimal renovation sequence, start date, and crew formation to minimise rental income loss and total renovation costs. Using a genetic algorithm, the time required to identify the optimal renovation plan was reduced from five years to just 25 minutes. The results were impressive: a 4% reduction in total renovation costs and a 70% reduction in rental income losses, convincing the property owner to proceed with the deep renovation.
Conclusion
The construction industry stands at the cusp of a digital revolution, with digital twins and AI playing pivotal roles in enhancing efficiency and cost-effectiveness. Dr. Mansour AlOtaibi’s insights underscore the importance of adopting these technologies to address the challenges of project progress, scheduling, and cost monitoring. As the industry continues to evolve, the integration of digital twins will be instrumental in creating the efficient, connected, and sustainable facilities of the future.
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