From 38138c7c984cc189867c45621cab6ee0fa168e0e Mon Sep 17 00:00:00 2001 From: nuluh Date: Thu, 15 May 2025 06:38:27 +0700 Subject: [PATCH] feat(latex): add initial literature review paragraph from paper which data I used #67 --- .../02_literature_review/literature_review/abdeljaber2017.tex | 3 +++ 1 file changed, 3 insertions(+) diff --git a/latex/chapters/id/02_literature_review/literature_review/abdeljaber2017.tex b/latex/chapters/id/02_literature_review/literature_review/abdeljaber2017.tex index e69de29..beeb3ad 100644 --- a/latex/chapters/id/02_literature_review/literature_review/abdeljaber2017.tex +++ b/latex/chapters/id/02_literature_review/literature_review/abdeljaber2017.tex @@ -0,0 +1,3 @@ +Traditional structural health monitoring methods often rely on hand-crafted features and manually tuned classifiers, which pose challenges in terms of generalization, reliability, and computational efficiency. As highlighted by [Author(s), Year], these approaches frequently require a trial-and-error process for feature and classifier selection, which not only reduces their robustness across structures but also hinders their deployment in real-time applications due to the computational load of the feature extraction phase. + +[Author(s), Year] introduced a CNN-based structural damage detection approach validated through a large-scale grandstand simulator at Qatar University. The structure, designed to replicate modern stadiums, was equipped with 30 accelerometers and subjected to controlled damage by loosening beam-to-girder bolts. Acceleration data, collected under band-limited white noise excitation and sampled at 1024 Hz, were segmented into 128-sample frames for training localized 1-D CNNs—one per joint—creating a decentralized detection system. Across two experimental phases, involving both partial and full-structure monitoring, the method demonstrated high accuracy in damage localization, achieving a training classification error of just 0.54\%. While performance remained strong even under double-damage scenarios, some misclassifications occurred in symmetric or adjacent damage cases. Overall, the proposed method presents a highly efficient and accurate solution for real-time SHM applications. \ No newline at end of file