Despite the benefits of using AI and language models in structural engineering, there are also potential risks that must be considered. One of the biggest risks is the potential for errors in the AI or language model. If the AI or language model is not properly trained or programmed, it could make mistakes that could lead to serious safety issues in the building structure.
Another risk of using AI and language models is the potential for bias in the data used to train the model. If the data used to train the model is biased in some way, this could lead to biased or inaccurate results when the model is used to design a building structure.
Structural engineers can use several methods to discern whether or not generative pre-trained transformer (GPT) or similar language models are providing correct information on a subject related to design. Here are some ways to ensure the accuracy of information provided by AI in the design process:
Validation and verification: Structural engineers can validate and verify the information provided by the AI using established principles and data sources. They can cross-check the AI's output against existing building codes, standards, and regulations to ensure that the design meets the required standards.
Expert review: Engineers can also seek the assistance of domain experts in the field, such as other experienced engineers or architects, to validate the design or provide feedback on the AI's output. This can help confirm the accuracy of the design and provide suggestions for improvement.
Testing and simulation: Engineers can use physical testing and simulations to verify the accuracy of the AI's output. They can run simulations of the building design under different conditions to assess its performance and compare the results with the AI's predictions.
Data quality assessment: Engineers can assess the quality of the data used by the AI, ensuring that the data is relevant and reliable. They can identify potential sources of bias or inaccuracies in the data and adjust the AI's parameters accordingly.
Regular calibration: Finally, engineers can regularly calibrate the AI to ensure that it remains accurate and up-to-date. This involves periodically checking the AI's performance against the validation criteria, making any necessary adjustments or improvements, and revalidating the design as needed.
Overall, it's essential for engineers to critically evaluate the output provided by AI and language models and ensure that they use appropriate validation and verification techniques to confirm the accuracy of the information provided. By implementing these methods, structural engineers can confidently integrate AI into the design process, helping to improve design quality and efficiency.