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Beyond development: Challenges in deploying machine learning models for structural engineering applications
Machine learning (ML)-based solutions are rapidly changing the landscape of many fields, including structural engineering.
Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge
This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their responses in handling fire safety related queries.
SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning
The field of machine learning (ML) has witnessed significant advancements in recent years.
Can AI Chatbots Pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) Structural Exams?
Our report shows that ChatGPT-4 and Bard, respectively scored 70. 9% and 39. 2% in the FE exam and 46. 2% and 41% in the PE exam.
Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering
This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality.
Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge
This paper presents an approach that leverages causal discovery and causal inference to evaluate the fire resistance of structural members.
Causality, Causal Discovery, and Causal Inference in Structural Engineering
This paper builds a case for causal discovery and causal inference and contrasts that against traditional machine learning approaches; all from a civil and structural engineering perspective.
Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence
The first section outlines big ideas pertaining to AI, and answers some of the burning questions with regard to the merit of adopting AI in our domain.
Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columns
This paper presents the development of systematic machine learning (ML) approach to enable explainable and rapid assessment of fire resistance and fire-induced spalling of reinforced concrete (RC) columns.
Insights into Performance Fitness and Error Metrics for Machine Learning
Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis.
