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An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference
Abstract While artificial intelligence (AI), and by extension machine learning (ML), continues to be adopted in parallel engineering disciplines, the integration of AI/ML into the structural engineering domain remains minutus. This resistance towards AI and ML primarily stems from two folds: 1) the fact that coding/programming is not a frequent element in structural engineering curricula, and 2) these methods are displayed as blackboxes; the opposite of that often favored by structural engineering education and industry (i.e., testing, empirical analysis, numerical simulation, etc.). Naturally, structural engineers are reluctant to leverage AI/ML during their tenure as such technology is viewed as opaque. In the rare instances of engineers adopting AI/ML, a clear emphasis is displayed towards chasing goodness metrics to imply “viable” inference. However, and just like the notion of correlation does not infer causation, forced goodness is prone to indicate a false sense of inference. To overcome this challenge, this paper advocates for a modern form of AI, one that is humanly explainable; thereby eXplainable Artificial Intelligence (XAI) and interpretable machine learning (IML). Thus, this work dives into the inner workings of a typical analysis to demystify how AI/ML model predictions can be evaluated and interpreted through a collection of agnostic methods (e.g., feature importance, partial dependence plots, feature interactions, SHAP (SHapley Additive exPlanations), and surrogates) via a thorough examination of a case study carried out on a comprehensive database compiled on reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composite laminates. In this case study, three algorithms, namely: Extreme Gradient Boosted Trees (ExGBT), Light gradient boosted trees (LGBT), and Keras Deep Neural Networks (KDNN), are applied to predict the maximum moment capacity of FRP-strengthened beams and the propensity of the FRP system to fail under various mechanisms. Finally, a philosophical engineering perspective into future research directions pertaining to this domain is presented and articulated.
Highlights Explainable AI and interpretable ML are discussed from a structural engineering perspective. Three algorithms are examined. Five explainability methods are explored in depth. Potential implications of XAI and IML in engineering contexts are discussed.
An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference
Abstract While artificial intelligence (AI), and by extension machine learning (ML), continues to be adopted in parallel engineering disciplines, the integration of AI/ML into the structural engineering domain remains minutus. This resistance towards AI and ML primarily stems from two folds: 1) the fact that coding/programming is not a frequent element in structural engineering curricula, and 2) these methods are displayed as blackboxes; the opposite of that often favored by structural engineering education and industry (i.e., testing, empirical analysis, numerical simulation, etc.). Naturally, structural engineers are reluctant to leverage AI/ML during their tenure as such technology is viewed as opaque. In the rare instances of engineers adopting AI/ML, a clear emphasis is displayed towards chasing goodness metrics to imply “viable” inference. However, and just like the notion of correlation does not infer causation, forced goodness is prone to indicate a false sense of inference. To overcome this challenge, this paper advocates for a modern form of AI, one that is humanly explainable; thereby eXplainable Artificial Intelligence (XAI) and interpretable machine learning (IML). Thus, this work dives into the inner workings of a typical analysis to demystify how AI/ML model predictions can be evaluated and interpreted through a collection of agnostic methods (e.g., feature importance, partial dependence plots, feature interactions, SHAP (SHapley Additive exPlanations), and surrogates) via a thorough examination of a case study carried out on a comprehensive database compiled on reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composite laminates. In this case study, three algorithms, namely: Extreme Gradient Boosted Trees (ExGBT), Light gradient boosted trees (LGBT), and Keras Deep Neural Networks (KDNN), are applied to predict the maximum moment capacity of FRP-strengthened beams and the propensity of the FRP system to fail under various mechanisms. Finally, a philosophical engineering perspective into future research directions pertaining to this domain is presented and articulated.
Highlights Explainable AI and interpretable ML are discussed from a structural engineering perspective. Three algorithms are examined. Five explainability methods are explored in depth. Potential implications of XAI and IML in engineering contexts are discussed.
An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference
Naser, M.Z. (author)
2021-06-26
Article (Journal)
Electronic Resource
English
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