The Power of AI: The construction industry
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Sustainable Growth and Tech Trends
Publication | Update: Sep 2020
According to Sameer Dhanrajani,of all the game-changing innovations driven by technology and artificial intelligence, the potential of the construction industry remains untapped.
According to McKinsey, the engineering and construction sector globally is valued to be worth $ 10 tn per year. While that is a respectable size, the construction industry overall has largely been slow in the uptake of inventions in the technology arena. In fact, several construction business houses in India tend to be family-owned and extremely traditionally-run, and have tremendous inertia in embracing new age technologies.
However, the past few years is seeing a change in the way construction firms operate. While McKinsey expects the permeation of AI in the construction industry to be modest right now, it does represent an opportunity for early adopters to catch the bull by the horns and build a sizeable competitive advantage.
According to the Market Research Future “Global AI in Construction Market Research Report” AI-enabled applications in the construction sector have been at a burgeoning stage and are presumed to witness their integration with building information modelling (BIM) and GPS which would be very beneficial for project planning, scheduling, and inventory purposes. Moreover, the AI is anticipated to support the construction sector in mitigating the existing and repetitive challenges of accuracy margin, project schedule delay, and safety concerns which are presumed to be one of the major propelling factors for the growth of AI in construction market over the assessment period. On the flip side, slow adoption rate of AI in the construction sector and complex algorithms for developing construction specific programs are expected to inhibit the growth of AI in construction market over the forecast period.
The AI in construction market is differentiated by stage, component, technology, deployment, organization size, and application.
On the basis of stage, the market is categorized into pre-construction, construction stage, and post-construction. By component, the AI in construction market is sub-segmented as solutions and services. The solution segment is categorized as virtual assistant, design & planning, revenue estimation, demand forecasting, predictive maintenance, and others. The service sub-segment comprised training & consulting, implementation services, and other support services.
On the basis of technology, the market is segregated into neural networks, machine learning & deep learning, and natural learning programming (NLP). Additionally, on the basis of deployment, the market is bifurcated into on-cloud and on-premise. Depending on the organization size, the market is classified as large enterprises, and small & medium enterprise. On the basis of application, the market is categorized as, building information management, schedule management, project management, risk management, equipment management, and supply chain management.
There are various avenues to explore the AI potential for the engineering & construction industry:
Image recognition for managing risk, safety and quality
AI-powered image recognition and algorithm accuracy in image detection has improved substantially due to advances in deep learning and neural networks. Numerous use cases of image recognition and video analytics are now being seen in areas as diverse as retail, consumer goods, insurance, healthcare, etc.
The construction industry would do well to adopt these techniques and apply them to manage risk and worker safety. Working conditions in the construction industry for labourers tend to be managed on an adhoc basis at present. Other cases also include unsafe working environments where certain infrastructure in overall construction projects are unsafe for human work.
Construction companies could employ drones to capture images and videos of their construction sites on a continuous basis. By applying deep learning and other AI techniques, firms would be able to identify unsafe workplace behaviour as well as unsafe working environments and run training interventions to improve the safety quotient of their workplaces.
Continuous design optimisation
With data readily available for analysis, AI can help continuously optimize the design of each project. A recommender system-like approach would help contractors and engineers identify the right design as well as the materials required to execute it. Additionally, AI-powered technology could also help recommend architectural finishes based on the proposed design – thus enabling construction firms to finalize the design and material requirements early in the schedule, and finish construction faster.
Increasing talent retention and development
The construction sector is remarkably disorganized and heavily relies on contract labour for executing a project. While minor, the cost and time involved in fulfilling positions left by ex-labourers and training new entrants really adds up and reduces the overall efficiency in project management.
AI has been applied to talent retention and talent development use cases in multiple industries, and the same can be applied to the construction industry with relative ease. With unsupervised machine learning algorithms, contractors and their parent companies will be able to forecast talent shortage accurately, and plan to backfill labour resources efficiently. AI can also enable improved labour retention strategies by recommending best options for ensuring improved talent retention and availability.
Project schedule optimisation
Construction projects are typically long drawn with a sizeable period elapsing between envisioning the project to having it commercially ready. In this period, we often see many niggles with respect to the project schedule. Overuse of materials, time-consuming nature of restocking, people availability issues – all these can throw the overall project plan into disarray.
An AI-backed schedule optimisation and forecasting solution can reduce the time and cost overruns associated with these delays. Using the data from benchmarked previous projects, managers would be able to run sophisticated analyses using AI to forecast the timelines of the next activity in the construction cycle.
Preventive maintenance through AI
Maintenance in the construction industry happens largely at two levels. Firstly, it is the maintenance of a partially and incrementally developing property. The second is when the builder organisation is responsible for the continuing maintenance after it has been leased out to tenants. At both levels, maintenance can be a hugely cumbersome and time-consuming activity, albeit critical, that the construction company must perform in order for operations to move smoothly.
AI can complement human effort in the process of preventive maintenance. By using sensors and cameras as the data capture layer, and applying machine learning algorithms over the data, facility managers can monitor their property with greater ease and identify guided interventions on where maintenance activity is required. Using this data can be doubly productive as it will provide the system inputs on when routine maintenance activity for all the working components of a modern property are required, and schedule accordingly.
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