Systematic review of application of artificial intelligence tools in architectural, engineering and construction

Momade, Mohammed Hamza; Durdyev, Serdar; Estrella, Dave; Ismail, Syuhaida

doi:10.1108/febe-07-2021-0036

Cited by 19 publications

(10 citation statements)

References 85 publications

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“…From the definition of the SHAP given in (10), posing f (x ) = e(z ) and φ 0 = E x ( f (X)), we get that…”

Section: Explanation Visualizationmentioning

confidence: 99%

“…Globally, the impact of AI is more anticipated in crucial economic and social pillars, such as manufacturing, transportation, healthcare, business analytics, finance, and retail [3,4]. Likewise, research on AI stretches over other niche domains such as entertainment [5], law enforcement [6], security [7], safety [8], defense [9], construction [10], investment [11], and mining operations [12]. The list goes on with endless possibilities, with new fronts being opened by researchers daily.…”

Section: Introduction 1artificial Intelligencementioning

confidence: 99%

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Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

et al. 2022

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Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

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“…From the definition of the SHAP given in (10), posing f (x ) = e(z ) and φ 0 = E x ( f (X)), we get that…”

Section: Explanation Visualizationmentioning

confidence: 99%

Section: Introduction 1artificial Intelligencementioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Globally, the impact of AI is more anticipated in key economic and social pillars such as manufacturing, transportation, healthcare, business analytics, finance, and retails [3,4]. Likewise, research on AI stretches over other niche domains like entertainment [5], law enforcement [6], security [7], safety [8], defense [9], construction [10], investment [11], and mining operation [12]. The list goes on with the endless possibility, with new fronts being opened by researchers on daily basis.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…The description of the SHAP is provided next. Given the model of explanation, 𝑒 namely, the coalition vector, and 𝑧 ∈ {0,1} , with 𝑧 = 1 indicating that the feature is present in the coalition, while 𝑧 = 0 points to the contrary, 𝑁 is the maximum coalition size, we have that where, as mentioned, 𝜙 expressed in (10) is the Shapley value of feature 𝑗.…”

Section: Model Performance Assesment and Shap Explainabilitymentioning

confidence: 99%

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Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Nor¹,

Pedapati²,

Muhammad³

et al. 2022

Preprint

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: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

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Smart Communities and Cities as a Unified Concept

Ponce,

Peffer,

Mendez Garduno

et al. 2023

Studies in Big Data

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Systematic review of application of artificial intelligence tools in architectural, engineering and construction

Cited by 19 publications

References 85 publications

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Smart Communities and Cities as a Unified Concept

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