인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This article proposes a novel approach to automatically generate velocity reflectogram of Pile Integrity Testing using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) model. Conventional Low-Strain Integrity Testing (LSIT) accuracy relies significantly on expert interpretation of reflected wave signals and entails subjectivity as well as efficiency limitations. The purpose of this study is to develop an artificial intelligence system capable of learning wave propagation behavior from acceleration inputs and generating reflectogram that capture pile toe locations correctly, thereby reducing dependence on human experience. The proposed technique eliminates human error and increases both the reliability and efficiency of the model. The strategy involved the collection of LSIT data from several of Egypt's driven piles projects, followed by systematic preprocessing which converted raw acceleration signals into digitized velocity-time series. Several RNN-LSTM networks with various hidden layers and neurons were trained and optimized against performance including measures the coefficient of determination (R<sup>2</sup>), computational expense, and visual examination of reflectogram. The proposed six-layer, 32-neuron LSTM model achieved an optimum balance between accuracy and computational expense and yielded training and validation R<sup>2</sup> of 0.9126 and 0.8778, respectively, and demonstrated satisfactory predictive generalization. Visual examinations also guaranteed the validity of the model, where "Good" predictions for toe location were up to 84% for the validation set and 89.5% for the training set, while "Fair" and "Bad" predictions had an average of only 10% and 5%, respectively. The experiments demonstrate that the RNN-LSTM model effectively mimics human-generated reflectogram with high accuracy and low mis-adoption risk. Lastly, this research describes how deep learning, namely RNN-LSTM, presents an excellent alternative to the conventional generated reflectogram, greater reliability, and reduced reliance on human experience.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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