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Artificial intelligence (AI) coupled with machine learning and end-point devices such as IoT sensors and robotics within the architecture, engineering, and construction fields are becoming more mainstream with significant measurable benefits. These include better safety, reduced building costs, improved operational efficiencies, enhanced labor management, and more. Through the integrated use of Building Information Modeling (BIM) within most A/E firms today, efficient planning and design of buildings sequenced around how building systems such as mechanical, plumbing, structural, electrical, and technology, all interweave into the architecture allowing the construction teams ability to ensure better planning and construction of buildings by having direct access to the data points derived from the BIM process. By leveraging AI, the entire team can readily identify and mitigate clashes between different models generated by the various teams to keep costs and labor in line while preventing rework of high-volume areas. This type of clash detection leverages the power of machine learning to explore all of the various scenarios to optimize the best solutions and generate design alternatives.
This risk mitigation works to prevent cost overruns, better sequence contractors in the field, improve safety, and give real time feedback to the design team to more quickly address conflicts in design and constructability. AI can provide automatic scoring and assign priority to issues as they arise across several areas, including high-risk subcontractors, that free time of the design and construction teams to focus resources on the highest risk factors. In design, the use of AI provides the ability to mitigate risk before the project even heads to construction by leveraging machine learning to evaluate the constructability of the 3-D model at the component level looking for weakpoints that can be addressed early on in the process.
By mitigating risks, productivity is improved in both coordination among various subcontractors as well as the use and integration of automated construction platforms, such as self-driving construction machinery, that can accomplish repetitive tasks more efficiently than human counterparts. This also works to reduce job site injuries, improve subcontractor sequencing, and reduce overall delays in delivery. This frees up human workers to focus on tasks better suited for people rather than automation which can additionally address labor shortages due to geography, available skill set, or a tight market demand due to multiple projects in the same area. It also alleviates scheduling conflicts due to subs working on multiple projects simultaneously.
Leveraging AI could boost productivity by as much as 50 percent through real-time data analysis causing the AEC industry to take a harder look at AI implementation. In the design side, AI can automate many tasks such as the creation and maintenance of schedules, drawing repetitive building elements, generation of block diagrams and the like while the benefits to construction focus on better planning, staging, and labor usage. For example, an AI driven system using a camera can evaluate progress on a job site including location of workers and equipment that can immediately inform both the design team and the site supervisors of deficiencies in progress or labor. This could be because of weather delays, construction issues, or sequencing issues, but, by having this information, additional resources can be deployed rapidly, and problems can be mitigated quickly. This can be vital is remote or dense urban construction areas.
Additionally, AI can monitor and streamline the process of using prefabricated components within a building being constructed off-site typically by robotics. This assembly-line style of autonomous machinery can use data from the field to make adjustments to assure proper fit once the piece reaches the job site leaving humans to work to finish the details like plumbing, HVAC, electrical, and technology infrastructure instead of having to modify a prefab panel to fit if conditions had changed in the field.
All of these advancements and more, can help a team predict cost overruns based on factors such as size, contract type, and competence level of the team. By creating historical data, future projects benefit through machine learning techniques using predictive models that align realistic timelines for future projects. Additionally, the compiled data on a project carries forward into other projects through learning based on past rubrics and metrics helping train through AI in-person or remote to enhance their own skills and knowledge quickly. This reduces on-boarding new staff as well as leveraging the experience of others captured in data to address challenges the team may themselves be facing for the first time ultimately reducing the time to resolve by seeing how previous teams addressed it expediting project delivery.
This includes post-construction information collectable through sensors, drones, and IoT devices that use AI algorithms to gain valuable insights about the operations and performance of a built environment. This post-occupancy data can be used to access new construction techniques over time to develop further the inventory of known viable solutions over those whose don’t stand the test of time. It also can help address preventative maintenance needs based on this information which could even prevent a catastrophic failure of a component of system.
Leveraging AI could boost productivity by as much as 50 percent through real-time data analysis causing the AEC industry to take a harder look at AI implementation
Despite the prediction that artificial intelligence will shrink the labor force, AI will instead change what that labor force does and how well it performs. It also will help inform future projects by reducing errors, worksite injuries, and improve delivery. Ultimately each project must decide where the right application of AI needs to be to fit both the teams needs and the project’s. Early adopters will be the ones setting the pace at the onset with each new generation of AI technology while opening up new opportunities and pointing the way to future success.
Raymond Kent is a multiple award winning industry thought leader and Principal with DLR Group and founder of DLR Group’s Innovative Technology Design Group which specializes in technology ecospheres for the performing and cultural arts, healthcare, Government, higher education, hospitality, and corporate markets. He is a known author, lecturer, and presenter in many publications, prominent universities, and at major industry conferences.