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Ground movements due to deep excavations in Shanghai: Design charts

Malcolm D. BOLTON,Sze-Yue LAM,Paul J. VARDANEGA,Charles W. W. NG,Xianfeng MA

《结构与土木工程前沿（英文）》 2014年第8卷第3期页码 201-236 doi: 10.1007/s11709-014-0253-y

摘要： Recent research has clarified the sequence of ground deformation mechanisms that manifest themselves when excavations are made in soft ground. Furthermore, a new framework to describe the deformability of clays in the working stress range has been devised using a large database of previously published soil tests. This paper aims to capitalize on these advances, by analyzing an expanded database of ground movements associated with braced excavations in Shanghai. It is shown that conventional design charts fail to take account either of the characteristics of soil deformability or the relevant deformation mechanisms, and therefore introduce significant scatter. A new method of presentation is found which provides a set of design charts that clarify the influence of soil deformability, wall stiffness, and the geometry of the excavation in relation to the depth of soft ground.

关键词： Shanghai excavations mobilizable strength design dimensionless groups design charts

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Advanced finite element analysis of a complex deep excavation case history in Shanghai

Yuepeng DONG, Harvey BURD, Guy HOULSBY, Yongmao HOU

《结构与土木工程前沿（英文）》 2014年第8卷第1期页码 93-100 doi: 10.1007/s11709-014-0232-3

摘要： The construction of the North Square Shopping Center of the Shanghai South Railway Station is a large scale complex top-down deep excavation project. The excavation is adjacent to several current and newly planned Metro lines, and influenced by a neighboring Exchange Station excavation. The highly irregular geometry of this excavation greatly increases the complexity in 3D Finite Element modeling. The advanced numerical modeling described in this paper includes detailed structural and geotechnical behavior. Important features are considered in the analysis, e.g., 1) the small-strain stiffness of the soil, 2) the construction joints in the diaphragm wall, 3) the shrinkage in the concrete floor slabs and beams, 4) the complex construction sequences, and 5) the shape effect of the deep excavation. The numerical results agree well with the field data, and some valuable conclusions are generated.

关键词： advanced finite element analysis deep excavations case history small-strain stiffness

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Application of random set method in a deep excavation: based on a case study in Tehran cemented alluvium

Arash SEKHAVATIAN, Asskar Janalizadeh CHOOBBASTI

《结构与土木工程前沿（英文）》 2019年第13卷第1期页码 66-80 doi: 10.1007/s11709-018-0461-y

摘要： The design of high-rise buildings often necessitates ground excavation, where buildings are in close proximity to the construction, thus there is a potential for damage to these structures. This paper studies an efficient user-friendly framework for dealing with uncertainties in a deep excavation in layers of cemented coarse grained soil located in Tehran, Iran by non-deterministic Random Set (RS) method. In order to enhance the acceptability of the method among engineers, a pertinent code was written in FISH language of FLAC2D software which enables the designers to run all simulations simultaneously, without cumbersome procedure of changing input variables in every individual analysis. This could drastically decrease the computational effort and cost imposed to the project, which is of great importance especially to the owners. The results are presented in terms of probability of occurrence and most likely values of the horizontal displacement at top of the wall at every stage of construction. Moreover, a methodology for assessing the credibility of the uncertainty model is presented using a quality indicator. It was concluded that performing RS analysis before the beginning of every stage could cause great economical savings, while improving the safety of the project.

关键词： uncertainty reliability analysis deep excavations random set method finite difference method

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Digital image correlation-based structural state detection through deep learning

《结构与土木工程前沿（英文）》 2022年第16卷第1期页码 45-56 doi: 10.1007/s11709-021-0777-x

摘要： This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

关键词： structural state detection deep learning digital image correlation vibration signal steel frame

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Development and deep-sea exploration of the Haidou-1

《工程管理前沿（英文）》页码 546-549 doi: 10.1007/s42524-023-0260-6

摘要： Development and deep-sea exploration of the Haidou-1

关键词： hadal zone autonomous and remotely-operated vehicle integrated exploration operation deep dive exceeding 10000 meters

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction moving trajectory pipe jacking GRU deep learning

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年第17卷第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis

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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

《信息与电子工程前沿（英文）》 2015年第16卷第11期页码 930-939 doi: 10.1631/FITEE.1500125

摘要： Head pose estimation has been considered an important and challenging task in computer vision. In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network (DCNN) for 2D face images. We design an effective and simple method to roughly crop the face from the input image, maintaining the individual-relative facial features ratio. The method can be used in various poses. Then two convolutional neural networks are set up to train the head pose classifier and then compared with each other. The simpler one has six layers. It performs well on seven yaw poses but is somewhat unsatisfactory when mixed in two pitch poses. The other has eight layers and more pixels in input layers. It has better performance on more poses and more training samples. Before training the network, two reasonable strategies including shift and zoom are executed to prepare training samples. Finally, feature extraction filters are optimized together with the weight of the classification component through training, to minimize the classification error. Our method has been evaluated on the CAS-PEAL-R1, CMU PIE, and CUBIC FacePix databases. It has better performance than state-of-the-art methods for head pose estimation.

关键词： Head pose estimation Deep convolutional neural network Multiclass classification

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年第16卷第3期页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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Stability analysis on Tingzikou gravity dam along deep-seated weak planes during earthquake

Weiping HE, Yunlong HE

《结构与土木工程前沿（英文）》 2012年第6卷第1期页码 69-75 doi: 10.1007/s11709-012-0146-x

摘要： The stability of a gravity dam against sliding along deep-seated weak planes is a universal and important problem encountered in the construction of dams. There is no recommended method for stability analysis of the dam on deep-seated weak planes under earthquake condition in Chinese design codes. Taking Tingzikou dam as an example, the research in this paper is focused on searching a proper way to evaluate the seismic safety of the dam against sliding along deep-seated weak planes and the probable failure modes of dam on deep-seated weak planes during earthquake. It is concluded that there are two probable failure modes of the dam along the main weak geological planes in the foundation. In the first mode, the concrete tooth under the dam will be cut and then the dam together with part foundation will slide along the muddy layer; in the second mode, the dam together with part foundation will slide along the path consist of the weak rock layer under the tooth and the muddy layer downstream the tooth. While there is no geological structure planes to form the second slip surface, the intersection of the main and the second slip surface is 40 to 80 m downstream from dam toe, and the angle between the second slip surface and the horizontal plane probably be 25 to 45 degrees.

关键词： gravity dam deep-seated weak planes stability against sliding earthquake

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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

《医学前沿（英文）》 2020年第14卷第4期页码 450-469 doi: 10.1007/s11684-019-0726-4

摘要： As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

关键词： deep learning neural networks pulmonary medical image survey

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Theoretical and technological exploration of deep

Heping XIE, Yang JU, Shihua REN, Feng GAO, Jianzhong LIU, Yan ZHU

《能源前沿（英文）》 2019年第13卷第4期页码 603-611 doi: 10.1007/s11708-019-0643-x

摘要： Mining industries worldwide have inevitably resorted to exploiting resources from the deep underground. However, traditional mining methods can cause various problems, e.g., considerable mining difficulty, environmental degradations, and frequent disastrous accidents. To exploit deep resources in the future, the concept of mining must be reconsidered and innovative new theories, methods, and technologies must be applied. To effectively acquire coal resources deeper than 2000 m, new theoretical and technological concepts about deep fluidized mining are required. The limits of mining depth need to be broken to acquire deep-coal resources in an environmentally friendly, safe, and efficient manner. This is characterized by ‘There are no coal on the ground and no men in the coal mine’. First, this paper systematically explains deep fluidized coal mining. Then, it presents a new theoretical concept, including the theories of mining-induced rock mechanics, three-field visualization, multi-physics coupling for transformation, and mining, transformation and transport. It also presents key technological concepts, including those of intelligent, unmanned, and fluidized mining. Finally, this paper presents a strategic roadmap for deep fluidized coal mining. In summary, this paper develops new theoretical and technological systems for accomplishing groundbreaking innovations in mining technologies of coal resources in the deep underground.

关键词： coal resource deep in situ fluidized mining theoretical system key technologies strategic roadmap

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

《结构与土木工程前沿（英文）》 2022年第16卷第5期页码 564-575 doi: 10.1007/s11709-022-0829-x

摘要： The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

关键词： water conveyance tunnels siltation images remotely operated vehicles deep learning ensemble learning computer vision

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Deep eutectic solvent inclusions for high- composite dielectric elastomers

《化学科学与工程前沿（英文）》 2022年第16卷第6期页码 996-1002 doi: 10.1007/s11705-022-2138-2

摘要： Recent advances in novel electroactive devices have placed new requirements on material development. High-performance dielectric elastomers with good mechanical stretchability and high dielectric constant are under high demand. However, the current strategy for fabricating these materials suffers from high cost or low thermal stability, which greatly hinders large-scale industrial production. Herein, we have successfully developed a novel strategy for improving the dielectric constant of polymeric elastomers via deep eutectic solvent inclusion by taking advantage of the low cost, convenient and environmentally benign synthesis process and high ionic conductivity from deep eutectic solvents. The as-prepared composite elastomers showed good stretchability and a greatly enhanced dielectric constant with a negligible increase in dielectric dissipation. Moreover, we have proven the universality of our strategy by using different types of deep eutectic solvents. It is believed that low-cost, easy-synthesis and environmentally friendly deep eutectic solvents including composite elastomers are highly suitable for large-scale industrial production and can greatly broaden the application fields of dielectric elastomers.

关键词： composite materials deep eutectic solvent dielectric elastomer high dielectric constant