With the rise of machine learning (ML), its global market size is expected to accelerate to $432.63 billion by 2034, exhibiting a CAGR of 26.7% over the next 8 years.
Given the risk of non-compliance in construction, ML is becoming an important tool to keep projects in line with their respective regulatory obligations.
Compliance measures are essential to ensure the safety of workers throughout the life of a project, and failure to comply with regulatory frameworks can result in significant civil liability if oversights result in personal harm.
Preventable non-compliance can also result in fines on construction projects, making it essential to adhere to standards.
Compliance with the regulatory framework is essential at all times, as non-compliant work can make it more difficult to sell or rent the affected property.
But monitoring compliance can be challenging, especially across large projects with many moving parts. This is why it’s worth implementing a machine learning solution that supports your project from conception to completion.
With this in mind, let’s take a look at how ML is paving the way for around-the-clock compliance for construction companies.
1. Compliance dispute management
Because ML is a subset of artificial intelligence, it can work seamlessly with natural language processing (NLP) to scan digital models and documents against thousands of pages of code to instantly identify possible design inconsistencies, safety violations, and missing compliance documentation.
Machine learning can also maintain a “golden thread” of information through the generation of tamper-proof records, including compliance checks and design changes, helping to comply with Building Safety Regulatory Authority (BSR) standards.
ML can also analyze contracts in a way that helps identify issues across clauses, such as inconsistency and risk of dispute, before they become compliance issues.
AI-powered construction project management platforms like Quickbase use emerging technologies like ML to standardize inspection checklists, toolbox talks, and pre-work plans to facilitate compliance throughout the construction process.
2. Real-time safety monitoring
Another use of ML is in assisting with computer vision for safety compliance. AI drones can scan job sites in real-time to detect safety hazards such as improper use of personal protective equipment (PPE), unsafe worker positions, and missing safety nets.
Machine learning analyzes these live video feeds to identify potential hazards before they cause an incident, allowing site managers to intervene quickly.
Wearable technology works hand-in-hand with machine learning, as ML can collect large amounts of unstructured data to gain contextual insights. This can help alert decision makers to worker fatigue due to biometric data or environmental exposure, helping to reduce the risk of on-site accidents occurring.
3. Strengthening quality control
ML’s ability to work with sensors to analyze data, interpret images and detect structural issues can help form a fundamental level of support for quality control processes. Whether it’s concrete quality issues or improper alignment issues, machine learning can monitor for problems invisible to the human eye and prevent costly rework.
The technology can also evaluate supplier records and material data to identify possible points of failure prior to installation.
4. Risk prediction
There is no better technology than ML when it comes to learning from the past. Machine learning is a great tool for factoring historical project data, weather patterns, and supply chain logistics. This helps eliminate cost overruns, schedule delays, and promotes proactive precautions.
Compliance issues can also be caused by breakdowns within heavy equipment. By using algorithms to monitor vibration and temperature changes, ML can more carefully predict maintenance needs and ensure equipment is operating safely and always in compliance.
This makes maintenance more accurate and allows workers to continue using heavy equipment with confidence while having diagnostic tools at hand to investigate anomalies.
Strengthening compliance
For construction companies, compliance monitoring can seem like a balancing act, with considerations for staying up-to-date on the regulatory landscape and proactive controls to avoid unexpected non-conformance issues due to material or machinery failure.
Machine learning is quickly emerging as one of the industry’s most impactful technological advancements, allowing you to identify compliance risks invisible to the human eye and continuously monitor them using a variety of data sources to ensure jobsite safety.
With so many moving parts in construction project management, it can be extremely difficult to stay on top of the various teams and vendors involved in the process. With the help of AI and ML, balancing compliance management can be simplified at scale.
