Innovation has always been a driving force in Architecture, Engineering and Construction (AEC) disciplines. There is a continuous need to design and build faster with higher quality, and yet, traditionally, the industry has been slow to adopt emerging technologies. By embedding Artificial Intelligence (AI) into critical AEC functions, industry professionals can quickly realize their return on investment and drive their teams' competitive edge.
1. Design Optimization
Traditional engineering relies heavily on manual processes, which limits efficiency and can stifle creativity. AI can be used to generate design alternatives with layouts that are optimized based on specific project criteria, such as minimizing environmental impact or managing cost constraints. As stated by ArchDaily, using AI in this way can help designers "...focus on creativity, emotional intelligence, and strategic thinking---skills that AI cannot mimic." By using AI, engineers have more time to come up with novel ideas based on their intuition and unique life experiences.
Another advantage is simulation, where AI tools can model how designs perform under various scenarios, including structural loads and exposure to different environmental stressors. With simulation results, architects and engineers can make confident, data-driven decisions earlier in the design process. Ultimately, this supports the longevity and sustainability of our built infrastructure.
2. Project Management
Project management is a complex coordination of stakeholders, timelines, resources, and budgets. But with AI, project managers' to-do list can be simplified. Using historical data to identify data patterns and correlations, predictive analytics can forecast potential delays or cost overruns, providing early warning signals to get ahead of potential issues before they become a significant downstream delay. AI can also help with general administrative activities, such as generating reports, tracking project progress, and organizing emails. This immediately reduces some of the time burden of mundane tasks.
3. Cost Estimation
Accurate cost estimation is a non-negotiable for the success of any construction project. Underestimates can lead to budget overruns, and overestimating runs the risk of missing out on big business opportunities. Traditionally, this process relies on legacy information and can be time-consuming and error prone. With AI, this manual work is streamlined with improved accuracy. Machine learning can be used to analyze massive amounts of data, such as materials, labor, equipment, and market conditions, and adjust historical data with real-time variables to provide detailed cost projections. As conditions change, AI tools keep pace, which means simplified planning and bidding processes and no late-stage cost overruns.
4. Remote Monitoring
From preventing accidents to protecting equipment, remote monitoring of construction sites is becoming increasingly valuable, especially for large-scale projects. AI, combined with advanced Internet of Things (IoT) sensors and drones, gives AEC professionals a real-time, direct line of sight into construction progress and site activities/conditions.
AI-driven image recognition tools can analyze drone footage and other images to automatically detect potential issues, such as structural defects, unsafe working conditions, or deviations from the original design. This allows for quicker interventions, which minimizes the risk of costly delays or accidents. In addition, AI-powered sensors can detect temperature, humidity, vibrations, and other environmental conditions to help teams verify construction is progressing under optimal conditions. These systems can alert project managers if any variables fall outside acceptable ranges, which promotes real-time adjustments and reduces construction errors.
5. Quality Control
A huge aspect of quality control in engineering is code compliance checks, and ensuring that the safety, functionality, and reliability of a design conforms to the set of rules and standards established by local or state authorities. Obtaining plan permits is a challenging process often plagued by delays related to bureaucracy, complex requirements, variability in cited rules/regulations, personnel turnover, or outdated systems. Implementing a large language model (LLM) in the permit review process can reduce the long waiting periods between each stage by analyzing plans and providing immediate feedback on design errors based on applicable standards.
In relation to construction inspection, machine learning models can be trained on historical quality control data to predict the likelihood of defects based on current project conditions. This helps teams prioritize inspection of the identified areas that may be most susceptible to issues.
BRYX's catalog includes AI models that offer tangible time and cost savings. At gobryx.com, you’ll find resources about the tool(s) available to take your productivity to the next level.