How to Predict Risks on Construction Sites with AI Systems in Real Time

Safety in the construction sector has traditionally been a challenge managed reactively or through static protocols. However, the arrival of Artificial Intelligence is radically transforming this approach, moving from basic prevention to active prediction. Implementing real-time AI systems not only optimizes workflows but also becomes a vital tool for saving lives, anticipating hazards before they materialize into accidents.

Computer Vision: Digital Eyes That Never Rest

One of the most powerful applications is the use of cameras equipped with computer vision algorithms. These systems monitor the construction site 24/7, capable of automatically identifying whether personnel are wearing the appropriate Personal Protective Equipment (PPE), such as helmets and vests. Furthermore, they can detect unsafe behaviors, such as workers passing too close to heavy machinery in motion or under suspended loads, sending immediate alerts to supervisors to stop the operation if necessary.

IoT Sensors and Predictive Data Analysis

Beyond the visual, the integration of IoT (Internet of Things) sensors in machinery and structures allows for the constant collection of data on vibrations, temperature, and material fatigue. AI analyzes these data streams in real-time to predict mechanical failures or structural collapses before they occur. By cross-referencing this data with accident histories and weather conditions, predictive models can suggest suspending certain activities during times of high probabilistic risk, ensuring data-driven decision-making rather than assumptions.

In conclusion, the adoption of AI for risk prediction in construction is not a futuristic technology but a current necessity for companies seeking to lead the market. At Codice AI, we understand that reducing accidents not only protects human capital but also financially protects projects by avoiding costly stoppages and litigation.

Key Points of the Article

• La visión por computadora detecta infracciones de seguridad y uso inadecuado de EPP en tiempo real.
• Los sensores IoT alimentan modelos predictivos que anticipan fallos en maquinaria y estructuras.
• La IA transforma la seguridad de un modelo reactivo a uno proactivo y preventivo.
• La reducción de accidentes mediante IA disminuye costos operativos y riesgos legales.