Artificial intelligence in the construction industry is here and is set to revolutionize the ways projects are planned, executed, and managed. From improving pre construction processes to enhancing on-site safety and project management, AI is already transforming the construction industry in ways we never imagined.
In this article, we will discuss the current applications of AI in various stages and processes of construction.
UPDATED AT: July 16, 2024
According to IBM, artificial intelligence, or AI, is "technology that enables computers and machines to simulate human intelligence and problem-solving capabilities." AI can be used in various scenarios, such as understanding and translating languages, analyzing data, and making recommendations.
In our complex, data-rich world, AI is very valuable to businesses that need to harness data, generate insights, and make informed decisions—including those in the construction industry. A 2023 report by Mordor Intelligence shows that the AI adoption in global construction is expected to reach USD 9.35 billion in the next five years, growing at a CAGR of 24.30%.
The pre-construction phase is crucial for the success of a project, as it lays the groundwork for all following stages. This phase involves detailed planning, cost estimation, risk assessment, and collaboration among stakeholders. Integrating AI into pre-construction makes this critical phase more efficient, accurate, and insightful.
The pre-design stage is focused on initial planning and conceptualizing the project. It involves setting project goals, conducting feasibility studies, and outlining the project scope. Preliminary designs and layouts are created to visualize the project concept.
AI is particularly useful in this early stage for assessing the feasibility of a project. For example, AI-powered virtual reality (VR) technology can create immersive visualizations of the project concept. This allows stakeholders to experience and evaluate the design before it is physically constructed, facilitating better decision-making and collaboration.
In this stage, the project's design is refined and detailed. Architects, engineers, and other professionals collaborate to create detailed drawings, specifications, and technical documentation. The goal is to turn conceptual ideas into concrete plans that meet requirements and regulatory standards.
AI offers new tools and techniques to boost the efficiency and quality of the design process. AI-powered Building Information Modeling (BIM) software can analyze and integrate design data from various disciplines (architectural, structural, and mechanical) and detect inconsistencies in real-time. Hochtief, one of the world's biggest construction companies, even has a sub company (Hochtief ViCon) where they develop and apply their own BIM 3D models.
Additionally, Autodesk’s generative design technology can create multiple design options for structures based on predefined goals and constraints.
During this stage, the design development is converted into comprehensive construction documents. These documents include contracts, and detailed architectural, structural, electrical, and mechanical plans, along with specifications for materials, finishes, and construction techniques. Construction documents serve as the basis for contractors to bid on the project and for obtaining necessary permits.
AI-based tools such as natural language processing (NLP) can be used to analyze project requirements and create a project scope document. This can help ensure that all project requirements are accurately captured and understood.
Aconex, for example, is a project management software that utilizes AI algorithms for data analysis, allowing project teams to collect and consolidate project data, such as documentation, drawings, and communications, in a centralized platform.
In this stage, the project owner asks contractors to submit bids based on the construction documents. Contractors review the project details, estimate costs, and submit their proposals. The owner then evaluates the bids and negotiates terms with the contractors.
AI can help by automating the evaluation of contractor bids. For example, Esti-mate uses AI-powered assists contractors in generating accurate cost estimates for their bids, enabling them to make competitive and profitable offers.
Moreover, AI-powered natural language processing (NLP) techniques can analyze and extract key information from complex contract documents. It enables efficient contract review and helps identify potential risks, obligations, and opportunities for negotiation.
Before construction starts, various permits and approvals are needed from authorities. This stage involves submitting construction documents to get permits for building, zoning, environmental compliance, and other regulations.
AI can help by automating the compliance checking process. It compares construction documents with building codes, zoning rules, and environmental requirements, identifying any issues and ensuring documents meet standards.
Additionally, AI algorithms trained on historical data can predict how long it will take to get permits and approvals. PermitFlow software platform speeds up the permitting process for applicants and city reviewers. This helps project teams plan and better manage project schedules.
The final stage of preconstruction involves tying up loose ends and preparing for the construction phase. This includes finalizing contracts, securing financing, establishing project schedules, and mobilizing resources. The project team ensures that all necessary preparations are in place to smoothly transition from pre construction to actual construction activities.
AI algorithms can analyze financial data, project budgets, and market trends to provide accurate financial projections and risk assessments. Also, AI-powered contract management systems such as Kira Systems can track key milestones, monitoring contract changes, and minimizing contractual risks.
AI enables proactive risk management, real-time monitoring, and accident prevention. Safesite, for example, utilizes AI algorithms to analyze data from various sources, including sensors, wearables, and CCTV cameras, to detect and predict potential safety hazards.
By monitoring factors such as worker behavior, equipment usage, and environmental conditions, AI algorithms can identify unsafe practices or conditions and provide real-time alerts to supervisors and workers.
Additionally, AI-powered computer vision systems can detect safety violations, such as workers not wearing appropriate personal protective equipment (PPE) or improper equipment usage.
AI applications in construction site safety offer data analytics and reporting functionalities, allowing companies to identify trends, assess safety performance, and implement targeted safety improvement measures.
Source: Key Labs
Maintaining quality control in the construction industry is essential but challenging due to the difficulty of detecting structural defects or deviations from plans, which can impact safety, timelines, and costs.
Traditional inspection methods are time-consuming and reliant on human expertise, which can be prone to oversights, especially in complex projects.
AI offers solutions through image recognition technologies, automating quality control by analyzing real-time site images. Trained on large datasets, these AI systems improve in accuracy over time, ensuring higher quality standards with enhanced efficiency. For example, Skanska, an international construction company, uses an AI system to identify quality issues, from incorrect installations to safety hazards, improving inspection accuracy and speed.
The unexpected failures of machinery breakdowns lead to delays in project schedules, often resulting in increased costs and inefficient resource allocation. The unpredictability of such failures complicates project management, making it challenging to plan activities and allocate resources.
Caterpillar Inc., the world's leading manufacturer of construction, reduced downtime caused by equipment failures and improved operational efficiency by applying AI-driven predictive maintenance technology.
AI algorithms analyze data from sensors installed on machinery to predict when equipment might fail or require maintenance. These sensors continuously gather data on parameters such as temperature, vibration, and sound, providing a comprehensive status of the machinery's health.
AI could be used to analyze data from building systems to optimize energy efficiency, indoor air quality, and other performance metrics.
Ctrack, for example, utilizes AI algorithms to collect and analyze data from vehicle telematics systems, including GPS tracking, engine diagnostics, and driver behavior. This allows fleet managers to monitor real-time vehicle locations, track fuel consumption, assess driver performance, and identify maintenance needs.
AI has shown significant potential in addressing inventory management and material tracking challenges on large construction sites. Traditional methods struggle with tracking the vast array equipment, often leading to delays and increased costs.
AI tools such as computer vision and machine learning offer innovative approaches to these problems. For example, Pix4D uses AI to create 3D models from imagery captured by drones, enhancing site monitoring and inventory management by identifying materials and equipment, assessing their condition, and tracking their location.
Similarly, Volvo Construction Equipment's Compact Assist tool uses AI algorithms to map compactor passes and record temperature maps for asphalt compactors, optimizing equipment usage and maintenance schedules.
The present and future of AI in the construction industry are promising as more AI-based tools and techniques become integrated into workflows. As technology continues to advance, AI is set to become an even more essential part of the construction process. It will help improve efficiency, reduce costs, and enhance overall construction performance.