Drawing reviews were done by professionals who would often spend significant hours scrutinizing drawings for safety and completeness. There was always the possibility of oversights and mistakes due to the complexity of design details and human fatigue. Professionals may inadvertently miss critical safety hazards or fail to ensure completeness due to exhaustion, distraction, or time constraints. The task would generally incur huge costs as it was labor intensive and demanded thorough examinations and the errors that led to far-reaching consequences, such as safety and the cost to rework during construction.

Machine Learning (ML) has stepped in as a transformative solution to the challenges posed by manual drawing reviews. ML models leverage algorithms and artificial intelligence that can analyze drawings with both speed and accuracy, thereby providing a more reliable and efficient means of identifying safety concerns and ensuring completeness. The algorithms can even detect potential risks, such as structural weaknesses or electrical issues while minimizing the likelihood of human errors and providing a consistent and thorough examination of each drawing.

Reviewing designs in construction and manufacturing is nothing short of tedious. This task involves rechecking regulatory standards, scrutinizing details for flaws, and the pressure of meeting deadlines. ML models trained to automate these tasks can automate routine checks in no time, allowing the designers to focus on the intricate parts of the project that require human expertise. Well-reviewed designs by the models can provide clarity and a streamlined workflow, which aids the team of designers, construction workers, and other stakeholders in completing the project well ahead of time.

Machine learning models can be trained to recognize patterns associated with safety hazards in drawings, ensuring that designs align with industry regulations and safety standards. Adhering to regulatory requirements and industry standards is paramount for any project. Automated error detection in the models minimizes the risk of oversights, providing a consistent and thorough examination of each drawing to prevent potential safety concerns. This safeguards the project not only from legal implications but also guarantees that the project meets the necessary safety benchmarks.

Implementing machine learning models in reviewing designs can significantly reduce the need for extensive manual labor billed on hours and does the job at almost zero cost. This can dramatically decrease the budget for the project. While the initial investment in ML technology is a consideration, the long-term financial benefits, including reduced rework costs and faster project completion, outweigh the upfront expenses and various other projects with similar datasets.