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AI can streamline operations in construction engineering management in multiple ways. Planning, building, operation, and maintenance are all changing the construction business. Artificial Intelligence is the backbone for establishing true digital initiatives in construction engineering management (CEM) to improve construction project performance. AI drives computers to perceive and acquire human-like inputs for perception, knowledge representation, reasoning, problem-solving, and planning, which can deal with difficult and ill-defined situations intelligently, and adaptively. AI investment is growing rapidly, with machine learning accounting for a large chunk to learn data from numerous sources and make smart, adaptive judgments.

AI can streamline operations in construction engineering management in multiple ways:


AI automates and objectivizes project management. AI-based technologies help traditional construction management overcome bias and confusion from manual observation and operation. Machine learning algorithms are used to intelligently study gathered data to uncover hidden information. They are also incorporated into project management software to automate data analysis and decision-making. Advanced analytics enable managers to better comprehend construction projects, codify tacit project knowledge, and quickly recognize project issues. Drones and sensors are used for on-site construction monitoring to automatically capture data and take images/videos of the site’s state, surroundings, and progress without human input. Such strategies may replace time-consuming, boring, and error-prone human observation.


AI approaches are also used to improve the efficiency and smoothness of building projects. Process mining uses AI to monitor critical procedures, anticipate deviations, uncover unseen bottlenecks, and extract cooperation patterns. Such information is crucial to project success and may optimize construction execution. Early troubleshooting choices may increase operational efficiency. It prevents expensive corrections afterward. Different forms of optimization algorithms are also a great tool for building up more believable construction designs. AI-powered robots are being used on construction sites to do repetitive activities like bricklaying, welding, and tiling. Smart machines can operate nonstop at almost the same pace and quality as humans, ensuring efficiency, productivity, and even profitability.

Computer Vision

The automated and robust computer vision techniques are gradually taken the place of laborious and unreliable visual inspection in civil infrastructure condition assessment. Current advances in computer vision techniques lie in the deep learning methods to automatically process, analyze, and understand the image and video annotations through end-to-end learning. Towards the goal of intelligent management in the construction project, computer vision is mainly used to perform visual tasks for two main purposes named inspection and monitoring, which can potentially promote the understanding of complex construction tasks or structural conditions comprehensively, rapidly, and reliably.

To be more specific, inspection applications perform automated damage detection, structural component recognition, unsafe behavior, and condition identification. Monitoring applications is a non-contact method to capture a quantitative understanding of the infrastructure status, such as estimating strain, displacement, cracks’ length, and width. To sum up, the vision-based methods in CEM are comparatively cost-effective, simple, efficient, and accurate, which can robustly translate image data into actional information for structural health evaluation and construction safety assurance.

Addressing Workforce Shortage

McKinsey predicted in 2017 that AI-enhanced analytics might boost construction efficiency by 50%. This is good news for construction businesses that can’t find enough human employees, (which is the norm). AI-powered robots like Boston Dynamics’ Spot the Dog help project managers assess numerous task sites in real-time, including whether to move personnel to different areas of projects or to other locations. Robot “dogs” monitor sites during and after work to locate pain locations.

Risk Mitigation and Safety

AI can monitor, detect, analyze, and anticipate possible risks in terms of safety, quality, efficiency, and cost across teams and work areas, even under high uncertainty]. Various AI methods, such as probabilistic models, fuzzy theory, machine learning, neural networks, and others, have been used to learn data from the construction site to capture interdependencies of causes and accidents, measure the probability of failure, and evaluate the risk from both a qualitative and quantitative perspective. They may overcome the ambiguity and subjectivity of conventional risk analysis.

AI-based risk analysis can provide assistive and predictive insights on critical issues, helping project managers quickly prioritize possible risks and determine proactive actions instead of reactions for risk mitigation, such as streamlining job site operations, adjusting staff arrangements, and keeping projects on time and within budget. AI enables early troubleshooting to avert failure and mishaps in complicated workflows. Robots can handle harmful tasks to reduce the number of people in danger at construction sites.

OSHA says construction workers are killed five times more than other employees. Accidents include falls, being hit by an item, electrocution, and “caught-in/between” situations when employees are squeezed, caught, squashed or pinched between objects, including equipment. Machine learning platforms like Newmetrix may detect dangers before accidents happen or examine areas after catastrophes. The program can monitor photographs and videos and use predictive analytics to indicate possible concerns for site administrators. Users may use a single dashboard to create reports on possible safety issues, such as dangerous scaffolding, standing water, and missing PPE like gloves, safety glasses, and hard helmets.

On-site threats include unsafe buildings and moving equipment. So, AI improves job site safety. More construction sites include cameras, IoT devices, and sensors to monitor activities. AI-enabled devices can monitor 24/7 without distraction. AI technologies can identify risky conduct and inform the construction crew via face and object recognition. This may save lives and boost efficiency while reducing liability.

Measuring Progress

Droxel uses AI to follow building projects and assess quality and progress in real-time. Droxel makes camera-equipped robots that can travel independently across building sites to acquire 3D “point clouds.” Doxel employs a neural network to cross-reference project data against BIM and bill of materials information after a digital model is ready. The collected information helps project managers monitor large-scale projects with thousands of elements. These insights include: how much is owing or whether the budget is in danger; projects’ timeliness; and early detection of quality issues which allows for correction and mitigation.

Improved 3D Modeling

BIM is a new (and better) approach to producing the 3D models that construction professionals use to design, build, and repair buildings. Today, BIM platform programmers include AI-driven functionalities. BIM uses tools and technology, like ML, to assist teams to minimize redundant effort. Sub-teams working on common projects typically duplicate others’ models. BIM “teaches” robots to apply algorithms to develop numerous designs. AI learns from each model iteration until it creates the perfect one.

BIM is at the center of a trend toward more digitization in the construction business, according to a Dodge Data and Autodesk report. Nearly half (47%) of “high-intensity” construction BIM users are close to completing digital transformation objectives.

The Future

AI may help eliminate a tech hurdle when working on one-off, customized projects, says AspenTech’s Paul Donnelly. AI can accelerate tech set up for new projects by using data from past projects and industry norms. This makes newer tech in construction feasible compared to when it must be manually set up for each job. Robotics, AI, and IoT can cut construction costs by 20%.

Virtual reality goggles let engineers send mini-robots to construction sites. These robots monitor progress using cameras. Modern structures employ AI to design electrical and plumbing systems. AI helps companies create workplace safety solutions. AI is utilized to monitor real-time human, machine, and object interactions and inform supervisors of possible safety, construction, and productivity concerns.

AI won’t replace humans despite forecasts of major employment losses. It will change construction business models, eliminate costly mistakes, decrease worker accidents, and improve building operations. Construction firm leaders should prioritize AI investments based on their individual demands. Early adopters will decide the industry’s short and long-term orientation.

The construction industry is on the cusp of digitization, which will disrupt existing procedures while also presenting several opportunities. Artificial intelligence is predicted to improve efficiency across the whole value chain, from building materials manufacturing to design, planning, and construction, as well as facility management.

But, in your firm, how can you get the most out of AI? The advantages range from simple spam email screening to comprehensive safety monitoring. The construction sector has only scratched the surface of AI applications. This technology aids in the reduction of physical labor, the reduction of risk, the reduction of human mistakes, and the freeing up of time for other vital duties. AI allows teams to focus on the most critical, strategic aspects of their work. At its best, AI and machine learning can assist us in becoming our best selves.