How AI and IoT Provide Value in Construction

Internet of Things (IoT) sensors predominantly provide visibility to an operating stack - enabling access to real-time and accurate operational data. Laying analysis on top of that data produces dashboards and other visual representations but artificial intelligence (AI) extends this further by harnessing the data streams to train models and identify patterns. Observations can then be made by a computer much like a human analyst could but at tremendous speed and scale. AI makes it possible to anticipate and predict events in a robust and scalable way. This can create huge business advantages. In this article, we'll look at applications of AI and IoT in construction.

Using IoT technologies to transform the construction industry creates pathways to higher productivity, quality, and safety in the construction processes together with lower environmental impact, and to increased sustainability, fitness-for-use and resilience in the resulting infrastructure. While notable gains are being made possible by the full deployment of IoT, in the longer term it is data-driven techniques and models based on AI and its subsets (machine learning, deep learning, and computer vision) that will deliver lasting value in the construction industry.

It is desirable to improve construction processes and save time on menial or time-consuming tasks in addition to reducing the physical workload on the construction site through automation and mass customization thereby removing barriers to cost reduction in the industry. Optimization remains an issue across the value chain, from design to demolition. Procurement rules especially in the public sector have been static over the years and are still restricted to the selection of the lowest bid and leave too little room for creativity and innovations, thus slowing the rate of return on experience and learning curves.

The potential applications of artificial intelligence (AI) in construction are broad. With requests for information, open issues, change orders, equipment and worker monitoring, materials delivery, claims management, progress tracking and production analysis as standard in the industry, AI is like a smart assistant that can scrutinize this mountain of data and alerts project managers about the critical things that need their attention and also highlight easy levers to pull so as to improve outcomes.

What is AI?

In simple terms, AI is computational software that learns from data (gathered from sensors, humans, and experiences) and can perform tasks that normally require different forms of natural intelligence. It is a field of computer science that includes several approaches and techniques, such as machine learning (of which deep learning and reinforcement learning are specific examples), machine reasoning (which includes planning, scheduling, knowledge representation and reasoning, search, and optimization), and robotics. Adoption of AI in the construction process can deliver insight and analysis, assisting to optimize, monitor and predict the built environment.

The declining costs of IoT devices and the availability of optimized connectivity options like 5G enable the gathering of the critical mass of data required by AI to deliver on its potential. Firms need a significant amount of data (in this case projects related) to feed into AI/ML models to drive more accurate decision-making. Companies that are early adopters of IoT on a large scale get to accumulate data faster and benefit from the power of AI.

In particular, data collected from sensors can be used to analyze machine performance for ad-hoc services such as predictive maintenance and fleet management or aid to better investigate problems and inefficiencies on the construction site. AI can help the construction industry automate its workflows, further improving efficiency and helping make suggestions based on historical data trends to improve the quality of work. Using AI can help prevent cost overruns, assist in the design offering generative design, help to educate workers creating safer working environments, help reduce costs by maximizing efficiencies and much more. AI and IoT technologies serve to help organizations generate, understand, and act on data so they can make better business decisions.

Potential Applications of AI and IoT in Construction

AI Applications in construction are gaining market traction and attention; from geotechnical engineering for a soil analysis to throwaway sensors and algorithms that can more accurately predict concrete curing times.

A few early-stage examples for firms to take advantage of the promise of AI range from lowering project costs and driving efficiency, ensuring on-schedule production and delivery, remote performance monitoring, fast dispute resolution, and improving safety and compliance.

Optimization

Schedule optimization can consider a wide variety of alternatives for project delivery and continuously enhance overall project planning. Using IoT sensors for remote monitoring of construction equipment coupled with the ability to perform analytics of status and location can be employed to reduce costs, improve energy efficiency and limit the idle time of machines. An AI technique like reinforcement learning, which allows algorithms to learn based on trial and error, has the potential to improve project planning and scheduling through enabling the assessment of endless combinations and alternatives based on similar projects, optimizing the best path and self-correcting over time. Additionally, the increased prevalence of modularization and prefabrication in project delivery results in the movement of large quantities of materials to job sites. This makes the need for enhanced supply chain coordination through supervised learning applications critical to control costs and overall cash flows.

Predictive Maintenance

Predictive applications can forecast project risks, constructability, and the structural stability of various technical solutions, providing insight during the decision-making phase and potentially saving millions of dollars down the road. Furthermore, these applications can enable the testing of various materials, limiting the downtime of certain structures during an inspection. The information gathered through data collection and analytics can be leveraged to speed up processes, reduce costs, improve energy efficiency, and apply computer vision to track defects in critical structures. Enhanced analytics platforms can collect and analyze data from sensors to understand signals and patterns to deploy real-time solutions, cut costs, prioritize preventative maintenance, and prevent unplanned downtime.

Increased Efficiency and Improved Productivity

Consistent reduction in waste (i.e. fuel, electricity, water) through real-time monitoring and analysis enables improvement of energy efficiency throughout the construction phases. Moreover, digitalization can be seen as a means of increasing turnover thanks to enhanced productivity and higher customization. Digital technologies like IoT and AI can help companies provide better products or services with fewer resources. Data analysis plays a central role in this, resulting in an in-depth understanding of the ecosystem to optimize processes and the use of machines. The extensive use of IoT to monitor construction processes and critical data analysis in every phase can lead to better management of resources resulting in a reduction in costs and an increase in profits.

Environmental Sustainability and Noise Reduction

The building process is a significant contributor to the carbon footprint of the construction industry and use of digital technologies like IoT (to deliver situational awareness of waste, emissions, and noise) and AI might lead to the adoption of new solutions that improve environmental sustainability (i.e. an improved use of resources and the shift towards hybrid or electrified machines — where needed — can lead to a significant reduction in CO2 emissions and noise).

Increased On-Site Safety and Compliance

Automation and building techniques like modular construction take major production processes offsite resulting in a reduction in physical workload and exposure to dangerous activities on the job site. IP cameras, drone imagery, and 3-D-generated models can be used to collect vast amounts of images and videos on which AI can operate and address issues with quality control, such as defects arising in execution and structural health monitoring. Project managers can use this knowledge to compare developing and final design intents, or train unsafe practices early detection algorithms to identify hazards on the job site project sites based on millions of drone-collected images. Image recognition and classification can assess video data collected on worksites to identify unsafe worker behavior and aggregate this data to inform future training and education priorities. Data from IoT sensors can be collected and AI processed to mitigate exposure levels and keep workers safe and stay compliant with regulations.

Managing Complexity

Due to the ever-increasing complexity of tasks, project managers are now using IoT to collect data to keep everything in perspective. Following site needs and expectations through sensors and data analysis, it is possible to innovate and customize products and services according to the inputs coming from the job site. The job site is getting more connected (i.e. construction machines, operators, drones, other vehicles) and data from the construction site can be stored in a cloud platform for further analysis or analyzed at the edge during streaming, opening the way for the creation of new integrated products, services and support solutions across all phases of a construction project.

Higher Attractiveness to Younger Generations and Digital Talents

Construction is reeling under a skills deficit. The industry has an aging workforce. There is a shortage of workers because of a failure to attract new blood as it is perceived to be dirty, dangerous and dull. Digital technologies like AI, enabled by huge data availability through IoT make the construction industry better placed to attract younger workers who have grown up with technology, are comfortable with it and indeed expect it to be part of their working lives.

IoT coupled with data analytics and AI is used to extract the most relevant information from the construction environment and transform it into knowledge, to support companies in introducing innovative solutions aimed at improving processes and operations.

Conclusion

As projects get increasingly complex, schedules tighten and labor shortages stretch available resources, it is more important than ever to get current, accurate and complete actionable data from field operations into the hands of decision-makers. Artificial Intelligence (AI) represents methods and tools for the analysis and the use of site data to create new and innovative business solutions. Having access to data and knowing how to generate insights is a fundamental precondition for new operational opportunities to arise.

Early movers and fast-followers will set the direction of the industry and benefit in the short and long term.