This blog is part of a series you will find out that companies with high levels of performance visibility are well placed to deliver successful construction project

Advanced capabilities and collaboration

• 53% of UK and Ireland construction managers said they were using artificial intelligence (AI) and /or machine learning technologies to assist with visibility of performance. As with the deployment of business intelligence tools, those most likely to be deploying advanced tools were mainly larger firms with over 1,000 employees.

• 81% of those not using AI/machine learning would consider using them to help achieve visibility of performance.

• 26% of construction managers say they would definitely consider AI. Alongside Germany, the UK and Ireland were seen as the most likely to adopt AI or Machine Learning.

• 90% of firms with visibility of performance shared their metrics with their supply chains to improve project performance

  • 42% always
  • 49% sometimes
  • 54% of larger firms provide complete transparency.

A wide range of benefits from sharing was reported:

• reduced downtime/ delays mentioned by 36%, and
• increased overall efficiency and productivity also by 36%)
• better supply chain management by 35%.

Managers were also asked about their firms’ readiness to adopt new construction technologies such as:

• augmented or virtual reality (AR, VR),
• drones and
• 4D building information modelling (BIM) in the next five years.

Businesses in the UK and Ireland were ahead of all other regions surveyed

86% considered themselves

• very well prepared 36%
• moderately prepared 50%; again,
• among larger UK and Irish firms, the figure for “very well prepared” was higher, at 45%.

The least prepared regions in the survey were Canada where only 21% said they were “very prepared”) and Germany (23%).

The Benefits of AI in Construction

The construction industry represents 7% of the world's labour force. Immersive technologies, the cloud, and artificial intelligence are used by other industry sectors to transform their productivity performance. Construction, in comparison, has progressed slowly.

Productivity did not grow at all in the construction industry as it is under-digitized and is slow to adopt new technologies.

Adopting the latest technology can be daunting for teams. But machine learning and artificial intelligence are helping make job sites more efficient and saving money in the process. AI solutions that have made an impact in other industries are beginning to emerge in the construction industry.

What is Artificial Intelligence and Machine Learning in Construction?

In Artificial intelligence (AI) computers are mimicking human cognitive functions, like

• problem-solving,
• pattern recognition, and
• learning.

Machine learning is a subset of AI.

Computers use statistical techniques to learn from data, without being programmed.

AI and machine learning are already used in various software and give the users a competitive advantage in the market

AI and Machine Learning for Smart Construction

Here are few examples of where AI can help in construction:

• Requests for information,
• open issues, and
• change orders.

It analyses all the data and sends alerts to the various users showing them where they need to focus their attention.

Solving main construction issues with AI:

*Prevent cost overruns: *

1. based on factors like project size, contract type and the competence level of project managers. Data such as planned start and end dates are used by predictive models to propose realistic timelines for future projects.
2. AI can push to remote staff useful and targeted training material to improve relevant skills and knowledge quickly. Project delivery is expedited reducing the time to onboard new resources onto projects

Better Design

A multi-disciplined team of architects, engineers, and construction professionals get insights, from a 3D model, to efficiently plan, design, construct and manage buildings and infrastructure.

The challenge is to ensure that the different models from the sub-teams do not clash with each other.

Machine learning generates design alternatives to identify and mitigate clashes between the different models generated by the different teams in the planning and design phase to prevent rework.

Risk Mitigation

Risks in a construction project can be categorised as follows:

• Quality,
• Safety,
• Time, and
• Cost.

General contractors can use AI and machine learning:

• to monitor and prioritise risks on the job site,
• to allocate time and resources on the biggest risk factors,
• to automatically assign priority to issues.
• to rate subcontractors on a risk score to mitigate risks for multi-disciplines teams.

Project Planning

To solve late and over-budget construction projects, AI can:

• autonomously capture 3D scans of construction sites and then feeds that data into different sub-projects.
• the site management team only deals with small problems before they become major issues.
• In the future the reinforcement learning technique, algorithms learn on trial and error, will assess various combinations and alternatives from similar projects to optimise the best path and correct itself over time.

Productivity on-site

Self-driving construction machinery can perform repetitive tasks more efficiently than their human counterparts:

• pouring concrete,
• bricklaying,
• welding,
• demolition.
• Site Preparation:
  • excavation with autonomous bulldozers programmed by humans to reduce the overall time required to complete the project.
• track job site work in real-time,
• facial recognition, onsite cameras, and similar technologies assess worker productivity and conformance to procedures.

Safety

Construction experience 5 times more death than in other industries. the leading causes are:

• falls,
• struck by an object,
• electrocution, and
• caught-in/between.

AI can:

• analyse photos from job sites,
• scan them for safety hazards such as workers not wearing protective equipment
• correlate the images with accident records
• alert site management when an elevated threat is detected.

Labour Shortages

A McKinsey report says that construction firms could boost productivity by 50 percent through real-time analysis of data by:

• planning the distribution of labour and machinery across jobs.
• evaluating job progress and the location of workers and equipment
• optimising the allocation of workers and equipment to complete the project on schedule,
• avoiding project to falling behind by deploying additional labour.

Off-site Construction

Factories are equipped with autonomous robots, driven digitally by the 3D model, to produce components of an asset, which are then put together by human workers on-site.

Walls can be cut by autonomous machinery more efficiently than their human counterparts, workers on site will fit together Plumbing, HVAC, and Electrical systems.

*Big Data *

Data generated from images captured from mobile devices, drone videos, security sensors, building information modelling (BIM) represent a gold mine of information to analyse and get insights automatically generated from the data with the help of AI and machine learning systems.

In Use

BIM stores information about the structure of the building. AI can be used to:

• monitor developing problems, and
• offer solutions to prevent problems.

In Conclusion:

Sharing metrics with the supply chains will help you:

• improve project performance,
• reduce downtime/ delays
• increase overall efficiency and productivity
• improve supply chain management

Adopting new construction technologies such as, immersive technologies (AR, VR), drones, autonomous robots, 4D building information modelling (BIM), artificial intelligence (AI) will help you to:

• reduce costs,
• produce great designs,
• mitigate risks,
• improve planning, productivity, and safety, and
• manage data efficiently

Implementing BIM can be daunting, but Driving Vision is here to help you at the pace you are comfortable with. Get started by getting in touch now