Monitoring of employees and work progress
Executives may have knowledge of how many people are involved on a given project, but they do not have accurate information about how much work has been done and whether it has been done correctly at all. This is due to the complexity of the projects and the interdisciplinary nature of the teams, who work on a number of multiple components. With so many components to inspect, accurately tracking progress and assessing progress is impossible without specialized software. Without such systems, budgets and schedules can be significantly exceeded, leading to additional costs and delays. AI-based image recognition systems can provide decision-makers with almost real-time information on schedule, budget and quality performance.
Winning contracts and commercial advantage
AI and natural language processing (NLP) can learn from previous bids to identify key success factors for winning (or not) a tender, thereby increasing the likelihood of winning a customer, as well as the margin of a project.
AI systems allow to measure, for example, the amount of material stored at a construction site, check if there is a need to place a supplemental order, and calculate how many trucks are needed to carry the load.
Project data such as construction site monitoring and real-time predictive analytics can be used to bill subcontractors and assess the quality of work they have performed. In addition, some subcontractors may cause damage, leading to claims or delayed payments. Having detailed documentation can avoid these problems.
Wearing appropriate equipment is a necessity, not an option. AI systems can detect people and objects, making it possible to check whether a worker is wearing protective gear and inform security officials of violations. With the power of AI and machine learning, site managers can also better identify, prioritize and monitor ongoing risks not visible at first glance and make data-driven decisions. As a result, AI can help make construction sites safer, identify potential safety risks and send real-time warnings.
Image recognition through the use of AI and machine learning
Image recognition through the use of AI and machine learning also provides an opportunity to feed information to digital twins, as well as to compare on-site field conditions with plans (for example, supporting twin models). This makes it easier to optimize schedules in terms of task sequencing and meeting deadlines.
AI systems can recommend the best construction techniques for a site based on previous projects and plans created during the design phase. With this information, engineers can make important decisions guided by objective analysis and predictions rather than subjective judgment.
Optical Character Recognition
Such tools replace manual transcription of documents or redrawing, allowing to search them and find the necessary data.
Early warning systems
By training algorithms to identify patterns in the data, anomalies and problems can be detected and then alerts can be sent to key stakeholders about the situation on site.
Working on drone data
Using drones to collect accurate survey maps and aerial photos of a construction site, as well as remotely tracking progress, saves time and project costs. Drone inspections are 5 to 20 times faster than traditional methods involving rope access, scaffolding, lifts and booms. The use of artificial intelligence in drone software and data analysis platforms can enable a drone to fly around as far as 150 kilometers of construction site, take pictures and transmit them to a data analytics platform, and, after a short period of time, send the user very accurate reports, such as those showing the progress of work every 100 meters. In addition, aerial photos can give project managers a different perspective and help them spot potential problems that may not have been visible from the ground.