Preventing Cost Overruns

 Most mega projects are over budget, even with the best project teams. Projects use artificial neural networks to predict cost overruns based on factors such as project size, contract type, and  project manager skill level. Historical data, such as planned start and finish dates, are used by predictive models to present realistic timelines for future projects. AI gives employees remote access to real-world training materials, enabling them to rapidly improve their skills and knowledge. This reduces the time it takes to onboard a new resource into your project. This will speed up the execution of your project.

AI for Better Building Design with Generative Design.

 Building Information Modeling provides building, engineering, and construction professionals with efficient planning, design, construction, and management of buildings and infrastructure. A 3D model-based process that provides insights. To plan and design the construction of a

 project, the 3D model must correspond to architectural, engineering, mechanical, electrical, and plumbing (MEP) planning and the ordering of activities for each team. The challenge is to keep the different subteam models from colliding with each other.

 The industry is using machine learning in the form of AI-assisted generative design to identify and mitigate conflicts between  different models generated by  different teams and avoid rework. There is software that uses machine learning algorithms to look at all the variations of the solution and generate design alternatives. The user specifies model requirements and the generative design software creates a 3D model optimized to the constraints, learning from each iteration until  the ideal he model emerges.

Risk Mitigation

 All construction projects have various forms of risk, including: B. Quality, safety, time, and cost risks. The bigger the project, the greater the risk, with multiple subcontractors working on different transactions in parallel on the construction site. There are now AI and machine learning solutions  that general contractors use to monitor and prioritize risks in the field, allowing project teams to focus their limited time and resources on the biggest risk factors. Use AI  to automatically prioritize issues. Subcontractors are evaluated against a risk rating, allowing site administrators to work closely with high-risk teams to mitigate risk.

Project planning

 Founded in 2017, the construction intelligence company promises that its robots and artificial intelligence will be the key to solving construction project delays and  budget overruns. The company uses robots to autonomously capture 3D scans of construction sites and  feeds the data into  deep neural networks to categorize the progress of various subprojects. When things get out of hand, the management team can step in and fix small problems before they become big problems. Future algorithms will use  AI technology known as reinforcement learning. This technique allows the algorithm to be learned by trial and error. You can evaluate an infinite number of combinations and alternatives based on similar projects. It helps you plan your project by optimizing the best path and fixing it over time.

AI Increases Construction Site Productivity

 Some companies  are starting to offer self-propelled construction equipment, which can perform repetitive tasks more efficiently than humans. B. Concrete placement, masonry, welding, demolition work. Excavation and preparation work is  performed by autonomous or semi-autonomous bulldozers that can prepare construction sites to exact specifications with the help of  human programmers. This allows human workers to focus on the construction work itself, reducing the overall time required to complete the project. Project managers can also track  work in the field in real time. They use facial recognition, on-site cameras, and similar technologies to assess employee productivity and adherence to procedures.