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Construction industry innovators are widely recognized for their dedication and creativity. Technology has transformed how building projects are planned and completed over time; artificial intelligence (AI) integration may completely revolutionize construction safety by improving worker welfare while simultaneously decreasing risks.
This blog seeks to highlight artificial intelligence's applications and effects on construction safety, along with any implications for this sector.
Artificial Intelligence (AI)
What is Artificial Intelligence? AI (artificial intelligence) refers to technologies capable of performing or augmenting tasks that humans typically perform, like reasoning or problem-solving, at a scale and speed that humans cannot.
Cognitive tools used for AI include these same types of technologies. Here are four forms of AI:
- Machine Learning (ML): The ability of algorithms to recognize patterns within large amounts of data without direct instruction from humans.
- Deep Learning: Deep learning is a subfield of machine learning that uses more abstract variables like images or speech to analyze them.
- Computer Vision: Deep learning technologies use computer vision to recognize images or texts to extract meaning from them.
- Drones: Unpiloted flying robots that use machine perception technology to capture images in difficult-to-access places where humans would otherwise struggle.
Why Is Construction Safety Essential?
Safety has long been of primary importance in construction projects. With their increasing complexity, it has only grown more crucial.
Construction work involves numerous hazards for workers. Working at heights and operating heavy machines are just two areas in which accidents could arise daily on construction sites - thus, the importance of investing in advanced safety measures to safeguard lives and avoid injuries is unquestionable.
What Are Machine Learning And Artificial Intelligence In The Construction Industry?
Artificial Intelligence, commonly referred to as AI, refers to computer systems which mimic human cognitive functions such as problem solving, pattern recognition and learning.
Machine Learning is one form of AI; using statistical techniques it enables computer systems to automatically "learn" from data without explicit programming - becoming better at comprehending new insights as they receive additional exposure to more data.
Construction industry applications of machine learning involve more intricate questions and algorithms; for instance, machine learning programs might track progress of grading plans to identify schedule risks early.
Algorithms might 'question' about cut and fill volume measurements, machine uptime/downtime issues, weather patterns or previous projects before creating risk scores to decide whether notifications need to be sent out or not.
Implication Of Artificial Intelligence On Construction Safety
Unfortunately, the construction business and hazards go hand-in-hand. No matter your level of awareness, accidents still happen - from unsafe structure mechanisms to hazardous moving equipment; construction workers are exposed to danger at every corner.
Therefore, safety and security must always remain high on every construction company's priority list. Due to the complexity of working conditions on construction job sites and an unparalleled degree of vigilance on site, artificial intelligence for construction safety is considered the ultimate and effective means for ensuring safe workplace conditions.
Modern construction sites utilize cameras, IoT devices, and sensors that monitor every facet of construction operations.
At the same time, AI-enabled systems constantly watch ongoing processes without failing. Facial recognition technology enables AI systems to recognize unsafe behavior or potential dangers early, alerting team members of possible threats while simultaneously decreasing liability exposure and increasing productivity - essential elements in today's construction business environment.
AI's incredible results make it no secret that construction industry professionals have begun adopting various AI-based technologies.
While its benefits remain in their early stages, AI will soon drive advancements concerning structure and efficiency within construction industries worldwide. But AI in construction safety depends on organizational alignment and being willing to take risks that will enhance operations.
We should go beyond conventional thinking patterns - using old machinery that risks human lives should not be justified on humanitarian grounds alone.
Current Challenges Facing Construction Safety
Below are a few of the current construction safety challenges:
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Accidents And Fatalities: Construction sites are notoriously unsafe places, often leading to fatal accidents ranging from falls, struck-by incidents and caught-in-between accidents being leading causes of construction-related fatalities.
Effective solutions must be put forward that proactively mitigate risks.
- Safety Management Inefficiencies: Traditional safety management techniques often entail manual procedures, which can be time-consuming and error-prone; with real-time safety monitoring as well as timely responses to potential hazards necessitating real-time response mechanisms, it requires a more streamlined and effective strategy for dealing with safety.
- Lack Of Real-Time Monitoring: Supervisors and safety managers have the responsibility of overseeing the welfare of a large workforce; however, keeping track of everyone's safety on an individual basis in real-time can be challenging; there should therefore be an automated continuous monitoring system which delivers up-to-date safety data in real-time.
Read More: 3 Forms of AI — What's the Cost, Gain, and Impact?
Will AI Impact Construction Safety In The Future?
Construction safety professionals remain optimistic about how new technology will transform their roles as construction safety specialists.
AI won't replace human labor altogether; it opens opportunities to address root causes, preventive measures and other initiatives that truly make a difference in operations management.
The future of AI in construction safety depends on organizational alignment and willingness to take chances that could improve operations.
Safety professionals could use their influence with other leaders in understanding this potential opportunity - something we see as potentially being transformative of industry operations overall.
Ten AI-Powered Construction Examples
Here are some examples of AI in construction:
Keep Cost Overruns Within Budget
Most mega projects end up going over budget even with top project teams onboard, often by overruns as planned start and end dates approach.
Artificial Neural Networks can help predict cost overruns on projects using factors like project size, contract type and competence levels of project managers, as well as historical start/end date information and predictive models used on historical projects to predict real timelines for upcoming ones.
AI technology also facilitates staff remotely accessing real training material that enhances skills quickly, reducing onboarding times onto projects, thus speeding project delivery by an orderly process, ensuring fast project delivery of all its constituent parts.
AI For Building Design Via Generative Design
Building Information Modeling (BIM) is a three-dimensional model-based process used by architects, engineers and construction professionals to efficiently plan, design, construct and manage buildings and infrastructure projects.
When used for planning purposes on any given project, three-dimensional models must take into account architecture plans as well as mechanical and electrical plumbing (MEP) plans created by multiple teams collaborating in its development - the challenge lies in making sure these different subteam models don't collide.
The industry employs machine learning in the form of AI-powered generative design to detect and reduce clashes among models generated by various teams in order to avoid costly rework.
There is software that uses machine learning algorithms to explore all potential solutions before offering design alternatives; once users enter requirements into models, this type of generative design software generates 3D models optimized to the constraints, learning with every iteration until reaching its ideal form.
Risk Mitigation
Each construction project comes with some level of risk that includes quality, safety, time and cost issues. As projects get larger, there's increased exposure due to multiple subcontractors working concurrently on job sites.
Modern AI/ML solutions help general contractors monitor and prioritize these risks on site so they can allocate limited time and resources towards managing major risk factors; AI can automatically assign priority issues while subcontractors with high-risk scores can work more closely together on mitigating them with construction managers focusing on this aspect of project delivery.
Project Planning
One construction intelligence company promises that robots and artificial intelligence are key components to solving late or over-budget construction projects.
Robot-mounted drones capture 3D scans of construction sites using autonomous 3D scanners and feed this data into a deep neural network for classification by subproject stage. If things seem off track, the management team should step in immediately to address minor problems before they escalate further.
Algorithms in the future will use reinforcement learning - an AI technique that enables algorithms to learn through trial-and-error; it assesses endless combinations and alternatives related to similar projects while optimizing its path accordingly and correcting itself over time - for optimal project planning purposes.
AI Can Enhance Productivity On Job Sites
Companies are providing self-driving construction machinery to perform repetitive tasks more effectively than their human counterparts, including pouring concrete, bricklaying, welding and demolition.
Excavation and prep work is performed by autonomous or semi-autonomous bulldozers programmed with exact specifications by humans to prepare job sites using robotic bulldozers - freeing human workers for construction activities while shortening project timelines overall. Project managers can track job site work lives using facial recognition technology such as cameras in addition to real-time tracking to assess worker productivity as well as compliance with procedures.
Construction Safety Can Be Ensured Using AI For Construction Projects
Construction workers are killed five times more frequently on the job than other laborers, according to statistics.
Falls were identified as one of the main causes (excluding highway collisions ) of private sector deaths (excluding collisions ) within this industry sector, followed by strikes by objects, electrocution and becoming caught in/between as the major factors leading to deaths within this field of labor.
AI Will Address Labor Shortages
Labour shortages and the desire to increase industry productivity have inspired construction firms to invest in AI and data science technologies, according to McKinsey reports suggesting real-time analysis could boost construction firm productivity by 50% or more; some construction firms have even begun using machine learning AI software in order to better allocate labor and machinery across jobs.
An evaluation robot provides project managers with instantaneous insight into which job sites have sufficient workers and equipment available to them to complete projects on schedule and which may need extra manpower deployed immediately.
AI-enhanced robots like Spot the Dog make it possible for large contractors like Mortenson to complete more work in remote regions where skilled labor may be scarce, saving both time and money on labor expenses.
Off-Site Construction
Construction companies are increasingly turning to off-site factories staffed with autonomous robots for off-site construction work, using assembly-line style walls completed more quickly by automated machinery than their human counterparts and leaving humans to finish details such as plumbing, HVAC systems or electrical wiring once assembled on-site.
AI And Big Data Construction
AI systems have access to vast quantities of data created every day that they can utilize and learn from for continuous improvement, giving AI systems ample material from which they can learn and grow each day.
Every job site becomes an opportunity for data capture with images captured from mobile phones or drone videos captured with security sensors, as well as building information modeling (BIM). Construction industry professionals and customers can leverage insights generated through artificial intelligence systems such as machine learning systems in order to assess insights extracted from such pools of information for improved decision-making processes and customer experiences.
AI For Post-Construction
Building managers can take advantage of AI long after construction has concluded by collecting information through sensors, drones and wireless technologies that feedback information about structures like buildings, bridges, roads or anything built environment-related.
AI provides valuable insights about operation and performance that allow building managers to monitor potential problems as soon as they develop, determine when preventative maintenance needs to take place or event guide human behavior for enhanced security and safety.
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Conclusion
Artificial Intelligence solutions in construction safety hold great potential.
From real-time safety monitoring to predictive analytics, construction sites are becoming safer and more effective spaces. Adopting AI responsibly by prioritizing worker wellbeing could transform this sector and make it safer in future years.
Companies are increasingly turning to AI technology to develop safety systems for worksites. AI tracks real-time interactions among workers, machinery, and objects on-site, enabling the monitoring of personnel interactions with each other and identifying construction errors or productivity concerns in real-time.
It alerts supervisors of potential issues related to worker safety, construction errors, or productivity loss.
