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A spatiotemporal deep learning method for excavation-induced wall deflections

Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3327-3338页

作者： Yuanqin Tao Shaoxiang Zeng Honglei Sun Yuanqiang Cai Jinzhang Zhang Xiaodong Pan College of Civil Engineering Zhejiang University of TechnologyHangzhou310023China Ministry of Education(MOE)Key Laboratory of Soft Soils and Geoenvironmental Engineering(SSGeo) Zhejiang UniversityHangzhou310058China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering Tongji UniversityShanghai200092China

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical ***,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring ***,most models lack flexibility in providing predictions for multiple days after monitoring *** study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep *** model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)*** S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq *** excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion *** excavation project in Hangzhou,China,is used to illustrate the proposed *** results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)*** prediction model demonstrates a strong generalizability when applied to an adjacent *** on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.

关键词： Braced excavation Wall deflections Deep learning convolutional layer Long short-term memory(LSTM) Sequence to sequence(seq2seq)

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Efficient Image Captioning Based on Vision Transformer Models

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Computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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