When I was a young drafting intern out of high school, I spent long nights and weekends on the “boards”, updating last-minute drawing revisions. I recall my first calculator, purchased as I started the pursuit of my masters’ degree, and the delivery of our company’s first desktop computer several years later. Throughout my engineering career, I have witnessed the evolution of technology in the architectural and engineering design fields, as well as the innovations in materials and systems associated with the building industry. However, these past several years have seen accelerated advancements through the development of artificial intelligence (AI) tools and applications being used throughout the life cycle of buildings. The capabilities of this powerful technology are being employed from design through construction and during ongoing facilities’ operations and maintenance.
AI in Design
Architects have employed computer-aided drafting and building information modeling for over a decade in facility design. With AI these capabilities have been greatly enhanced. Architects are using various tools for design ideations and conceptual designs. Image diffusion applications like Midjourney, Stable Diffusion and Adobe Firefly can create various design images from text prompts. Related AI design packages that learn from metadata can assist in schematic designs, accommodating constraints from codes, regulations and ordinances. Related, but specialized AI tools can assist in residential and urban planning. AI-powered rendering services like ArkoAI can provide high quality, photorealistic perspectives within minutes.
Besides the design benefits of AI, architects are also using text generation AI tools such as ChatGPT to help write and edit reports, as well as prepare presentations for clients and develop fee proposals. There are also AI project management tools, like ClickUp, which have been adopted by professionals in many industries, including architecture.
Consulting engineers engaged in building design are employing AI as well. Structural engineers use AI algorithms to assist in generating optimal designs by evaluating numerous parameters and constraints. AI-driven structural analysis tools can simulate and evaluate complex structural behavior, helping engineers identify potential vulnerabilities, predict failure modes and optimize structural performance. These tools also consider factors such as material usage, cost-effectiveness, and structural performance. It is anticipated that AI will play an even greater role in designing efficient and sustainable structures, incorporating newly developed materials and innovative construction techniques.
Mechanical and energy engineers use AI to predict facility systems’ performance and ensure energy efficiency. While energy modeling software like EnergyPlus and eQuest have been employed to predict building energy usage and compare alternative material, equipment and system selections, designers are incorporating machine learning (ML) frameworks to enhance their modeling. Combining these simulation programs with historical weather data, material properties and occupancy patterns helps deliver more accurate energy loads and reduces the number of manual iterations, saving time and labor. Pairing similar simulation programs with programs that model renewable energy generation and battery storage, such as Homer Pro, can help optimize solar photovoltaic and associated storage system size to minimize reliance on the grid.
AI in Construction
AI has affected the construction industry as well. Contractors are using AI to improve project planning and scheduling, prevent cost overruns, mitigate risks, improve job site safety and increase productivity.
Several programs, like OpenSpace, Doxel and Buildots, use cameras to capture a comprehensive visual record of the project site. The cameras may be mounted on hardhats, drones or robots, and the captured data is analyzed using deep learning algorithms and then used to track the project’s progress, identify issues and potential safety concerns and to help with communication between team members. Other AI programs assist contractors with various aspects of construction management, including schedule evaluation, collaboration between project subcontractors, financial management and field productivity metrics.
AI in Operations
Once buildings are occupied and operational, AI provides a number of benefits for building owners and their facilities’ staff, including optimizing operations, improving decision-making and reducing costs. One of the keys is AI’s ability to provide data-driven insights. Analytical tools powered by AI can use massive amounts of data from numerous sources, including maintenance logs, energy demand and consumption records, past projects, Internet of Things (IoT) sensors and facility occupancy data.
Decreasing energy use is one of the largest opportunities for facility cost savings. By analyzing the granular demand and consumption data from submeters and power monitoring systems and managing the indoor environmental conditions based on occupancy, air quality, external temperatures and lighting requirements, AI-driven building automation systems (BAS) can drive cost savings for the owner or property management company. The latest BAS include modules for analyzing energy information (generating key performance indicators) and interfacing with monitoring-based commissioning systems, often featuring fault detection and diagnostic algorithms.
AI can assist owners, facility management staffs, and architect/engineer (A/E) teams in the planning and design of facility projects. By capturing current traffic patterns and using sensors to assess current space usage, AI can help identify underused or incorrectly used spaces, then develop alternative options related to space planning and related layouts. In addition, by analyzing data from previous construction and renovation projects, ML algorithms can help predict potential construction delays or issues. This will allow project managers to take proactive steps with respect to the scheduling of specific tasks and resources.
AI’s analysis of facility data permits managers to make informed decisions regarding the allocation of resources and maintenance management strategies for their building systems. Decisions regarding staffing levels and inventory management can be made using predictive modeling. In addition, historical data can help predict equipment failures and preventative maintenance needs, leading to improved operational efficiency, reliability and cost savings.
Challenges of AI
While the above outlines many advantages and opportunities associated with the expansion of AI in the building sector, several challenges exist. These involve a limited skilled workforce, the secure protection of data, ethical considerations regarding the transparency of the decision-making used by AI and AI’s computational power consumption.
Globally, AI and machine learning (ML) specialists are professions experiencing the fastest growth in demand. Roughly 60% of major firms surveyed in the U.S and U.K. reported a lack of staff with sufficient AI experience. The design, construction and facility management industries will need to compete in recruiting (and invest in training) qualified data programmers and AI specialists for their companies.
Similarly, A/E clients, contractors and building owners must be informed regarding the data collected by AI systems and its use, and they must feel confident that these systems have been secured to protect their personal information. Similarly, to foster trust with buildings’ tenants, facility managers need to be transparent and share what data will be collected from their businesses and what benefits can be expected by using AI.
While data center expansion has been one of the primary contributors for growth in domestic electrical consumption over the past several years, that growth is expected to accelerate with the expansion of AI servers over the next decade. A report from McKinsey & Co. forecasts the U.S. data center industry will more than double its present annual power consumption (from approximately 17 gigawatts to 35 gigawatts) by 2030. New data centers required to support generative AI technology must be designed to be energy efficient using the latest IT hardware.
Over the past five decades, we have seen remarkable technological advancements in the design, construction and facility management industries. For those of us who have witnessed these advancements, it has been an exciting time. For those just starting their careers in these fields, the future of AI promises even more excitement.
Robert Knoedler, P.E., CxA, EMP, will be on the panel discussing the Importance of AI at CxEnergy 2025, the premier conference & expo in commissioning and energy management (April 28-May 1, Charlotte, NC). Learn more at www.CxEnergy.com.