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Prediction of the slurry pressure and inversion of formation characteristics based on a machine learning algorithm during tunnelling in a fault fracture zone

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2024年 144卷

作者： Shen, Xiang Chen, Yifan Cao, Liqiang Chen, Xiangsheng Fu, Yanbin Hong, Chengyu Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Beijing Univ Technol Inst Geotech & Underground Engn Beijing 100124 Peoples R China Shenzhen Univ Key Lab Coastal Urban Resilient Infrastruct MOE Shenzhen 518060 Peoples R China Shenzhen Key Lab Green Efficient & Intelligent Con Shenzhen 518060 Guangdong Peoples R China

The reasonable setting of the slurry pressure is very important for the safety of shield tunnel construction. In view of the common geological problems of high water pressure and multiple fracture zones in water conveyance tunnels in China, the instability mechanism of the shield excavation face in these formations remains unclear. In this paper, based on the Pearl River Delta Water Resources Allocation Project, various machine learning algorithms are introduced, and the predicted values of the slurry pressure obtained with different machine learning algorithms are evaluated and compared. The optimal machine learning algorithm suitable for this project is determined. Then, using this algorithm, considering the influence of the fault fracture zone, the concept of the formation influence coefficient is proposed, which is used as the input of the prediction model. The influences of the formation coefficient distribution and the influence width of the fracture zone on the accuracy of slurry pressure prediction are examined. Finally, based on this model, the slurry pressure of nearly 100 groups is predicted and verified. The results show that (1) if the influence of the fault fracture zone is considered and a reasonable influence width of the fault fracture zone can be determined, the mean absolute error (MAE) of the predicted slurry pressure is much higher than that of the predictions without considering the fault fracture zone, and the prediction accuracy can be improved by approximately 25%;(2) when the formation influence coefficient is obtained via the method of the theoretical distribution and the influence width of the fault fracture zone is 17 rings, the prediction accuracy of the slurry pressure reaches the optimal value;and (3) through on-site refined investigation, it can be determined that the influence width of the fault fracture zone is 19 rings. The research results of this paper can provide an effective method for setting the shield slurry pressure under

关键词： Shield tunnel Fault fracture zone machine learning algorithm Slurry pressure Prediction and analysis

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Inversion of Chlorophyll-a Concentration in Donghu Lake Based on machine learning algorithm

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WATER 2021年第9期13卷 1179-1179页

作者： Tang, Xiaodong Huang, Mutao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 mu g/L and 12.66 mu g/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 mu g/L and 16.49 mu g/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

关键词： chlorophyll-a inversion machine learning algorithm Donghu Lake

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Improving the efficiency and stability of perovskite solar cell: Application of innovative machine learning algorithm

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第27期31卷 9270-9296页

作者： Li, Haiyang Diao, Xinliu Ragab, Adham E. Henan Polytech Inst Dept Basic Courses Nanyang 473000 Henan Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh Saudi Arabia

Solar cells are modern inventions that use the photovoltaic effect to directly convert light energy into electricity, generating electrical charges that are free to move through semiconductors. The semiconductor is typically utilized as the raw material for solar cells. In order to convert energy, electron-hole pairs that are responsible for producing light (photon) energy must be absorbed in a semiconductor, followed by charge carrier separation. Enhancing solar cells' stability is crucial in engineering because they are used in a variety of environments. Moreover, graphene nanoplatelets (GPLs) have a great deal of potential to enhance ceramic-GNP composites' mechanical, tribological, electrical, thermal, and biological characteristics, all at once. machine learning algorithms (MLA) are often used to forecast how various systems would behave. In an MLA network, hyperparameters like the number of hidden layers and learning rate are often selected manually as required. MLA is used in this work to examine spinning cylindrical constructions' stability at the microscale. In this context, the particle swarm optimization (PSO) is used to optimize the weights and biases of the network. The number of perceptions in the two hidden layers is optimized in a second parallel process using a genetic algorithm. The modified torque-stress theory (MCST) equation's numerical solution for the dynamic behavior of GPL-reinforced perovskite solar cells was used to train the MLA. It is proposed to guide the spatial discretization of governing equations using the variational differential quadrature (VDQ) method as a direct discretization of the energy functional in the space domain. Lastly, the findings demonstrate that the stability of the current cantilevered solar cell reinforced by GPLs is significantly influenced by curvature, length scale, the shape of the solar cell, and mode number factors.

关键词： Perovskite solar cells photovoltaics efficiency enhancement stability improvement machine learning algorithm

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MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF machine learning algorithm IN LULC CLASSIFICATION IN THE LARGE AREA USING SENTINEL-2 AND LANDSAT-8 DATASETS OF DEHRADUN AS A TEST CASE 24

MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF MACHINE ...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Srivastava, Anubhava Bharadwaj, Shruti Dubey, Rakesh Sharma, Vinarma Bhushan Biswas, Susham Rajiv Gandhi Inst Petr Technol Dept Comp Sci & Engn Amethi Uttar Pradesh India

In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency's "Sentinel" and American Earth observation satellite" landsat" missions have been used in various remote sensing *** Earth Engine (GEE) is such a tool that publicly allow the use of these available datasets, there is a large amount of available data in GEE, which are being used for computing and analysing purpose. In this article, we compare the classification performance of four supervised machine learning algorithms: Classification and Regression Tree (CART), Random forests (RF), Gradient tree boosting (GTB), Support vector machines (SVM). The study area is located at 30.3165 degrees N, 78.0322 degrees E near the Himalayan foothills, with four land-use land-cover (LULC) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 and LANDSAT-8 covering spring, summer, autumn, and winter conditions. Here we collected a total of 2084 sample points in which 536, 506, 505, 540 points belong to urban, water, forest and agriculture points respectively. which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, for accuracy measurement we use confusion and Cohen's kappa calculation *** have calculated CART (Accuracy 93.52% and Kappa coefficient 91.36%), Random Forest (Accuracy 95.86% and Kappa coefficient 94.48%),Gradient Tree Boost (Accuracy 95.33% and Kappa coefficient 93.37%),Support Vector machine (Accuracy 73.54% and Kappa coefficient 76.28%) for Landsat 8 data sets and CART (Accuracy 89.24% and Kappa coefficient 85.64%), Random Forest (Accuracy 91.45% and Kappa coefficient 88.59%),Gradient Tree Boost (Accuracy 87.71% and Kappa coefficient 83.58

关键词： Google Earth Engine Landsat Sentinel machine learning algorithm Classification

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Analysis of Main Control Factors of Horizontal Well Production Capacity based on machine learning algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2024年第3期10卷 228-237页

作者： Haibiao Wang Yancai Gao Zheng Yuan Kaixing Li Mingyue Pang

Accurately identifying the main controlling factors of the fracturing effect of horizontal wells in tight gas reservoirs, and then effectively guiding the optimisation of the fracturing scheme, is the key to enhance the fracturing capacity of horizontal wells in tight gas reservoirs. Relying on geological and engineering data of Block A of a tight gas reservoir, four algorithms, namely, Spearman coefficient, maximum mutual information coefficient, Copula entropy and grey correlation, were used to identify the main controlling factors affecting the fracturing effect of horizontal wells under different completion methods, and the weights of the four algorithms were weighted and combined in order to reduce the contingency of the evaluation results of a single weight, and the geological and engineering big data of the 126 barehole horizontal wells and 21 cased horizontal wells of this block were summed up. The study summarises the relationship between the geological and fracturing parameter characteristics of 126 openhole horizontal wells and 21 cased horizontal wells in the block and the test production of a single well, and puts forward suggestions for efficient development of horizontal wells at a later stage. The study shows that the formation pressure coefficient is the main controlling factor for determining the single-well productivity of horizontal wells in this zone, and the productivity of barehole completed horizontal wells is controlled by the fracturing fluid return rate, effective reservoir encounter rate and mud content, while the productivity of cased horizontal wells is controlled by the number of fractured sections, effective reservoir encounter rate and average section length.

关键词： Tight Gas Reservoir machine learning algorithm Horizontal Wells Production Capacity

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Evaluating the integrated performance and effectiveness of RUSLE through machine learning algorithm on soil erosion susceptibility in tropical plateau basin, India

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JOURNAL OF SEDIMENTARY ENVIRONMENTS 2024年第3期9卷 665-693页

作者： Kundu, Moumita Ghosh, Arnab Abu Zafor, Md Maiti, Ramkrishna Vidyasagar Univ Dept Geog Midnapore West Bengal India Integrated Inst Adv Res & Informat IIARI Res Incubat Ctr Kolkata West Bengal India Bangladesh Army Int Univ Sci & Technol Dept Civil Engn Cumilla Bangladesh

Soil erosion is a huge problem in any plateau region as the population increases with rapid urbanization, which, in turn, increases the amount of sedimentation in the river through the increasing amount of soil erosion in the plateau region. If different methods identify these soil erosion zones, it is possible to take various measures to prevent soil erosion. This article discusses the nature, extent, and rate of soil erosion and its spatial pattern along the Rupnarayan basin with the help of RUSLE (Revised Universal Soil Loss Equation) and a machine learning algorithm. This area is highly affected by monsoonal rainfall and changes in land use patterns over time. By discussing the performance of RUSLE through machine learning models i.e. Frequency Ratio (FR), Artificial neural network (ANN), support vector machine (SVM), Extreme Gradient Boosting (XGB) and ensemble model using some permutation combination of aforesaid model with various GIS (Geographical Information System) and spatial basis parameters, it is seen that the undulating upper portion of the region is the highest soil erosion susceptible zone. Except for any other GIS platform, these models are developed through Google Earth Engine and interpreted by SHapley Additive Explanation (SHAP), based on game theory. This study developed a new interpretable model in the field of soil erosion that is helpful for global and local representation. Erosional locations obtained from field studies are focused on achieving this goal. This study used 389 soil erosion points as a dependent factor against 14 soil-affecting independent factors. The best-performing model is determined from the AUC (Area Under the Curve) curve and kappa indices and interpreted globally and locally by the SHAP method to evaluate the effectiveness of each factor in soil erosion for the entire study area and the specific region. The result shows that the AUC values of FR, SVM, ANN, XGB and ensemble XGB-FR-SVM are 0.842, 0.831, and 0.828. 0.920

关键词： Soil erosion RUSLE machine learning algorithm Ensemble model GEE (google earth engine) SHAP (shapley additive explanation)

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Fiber grating loop ring-down strain sensors using overlap spectrum demodulation and machine learning algorithm

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OPTICAL FIBER TECHNOLOGY 2023年第1期76卷

作者： Chen, Dongying Zhang, Hao Xiao, Yiyi Zhang, Zilong Chen, Weijuan Huang, Shuyan Chen, Huaixi Fujian Jiangxia Univ Coll Elect Informat Sci Fuzhou 350108 Peoples R China Smart Home Informat Collect & Proc Internet Things Fuzhou 350108 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350007 Peoples R China Chinese Acad Sci Fujian Inst Res Struct Matter Fuzhou 350002 Peoples R China Fujian Jiangxia Univ Dept Elect Informat Sci Fuzhou 350007 Peoples R China

The fiber grating loop ring-down (FGLRD) can be used in strain sensors to reduce costs and enhance system sensitivity. Introducing the overlap spectrum demodulation technology in the FGLRD can improve the system's linearity and expand the measurement range. However, there is always strong amplified spontaneous emission (ASE) noise owing to erbium-doped fiber amplifiers (EDFAs) in the FGLRD structure. The noise increases the difficulty of detecting the output ring-down spectrum and reduces the detection accuracy. This study proposes a machine learning algorithm for data processing of chirped FGLRD strain sensors based on overlap spectrum demodulation. It uses a global average pooling one-dimensional convolutional neural network algorithm to construct a data recognition model. The experimental results show that the method can improve the recognition accuracy and reduce the recognition errors caused by ASE noise compared to the conventional ring-down spectral peak detection method. The model accuracy was 100 % for the training and test sets and 98 % for the validation set. The loss value was close to 0.06, and the recognition time was approximately 0.5 ms.

关键词： Fiber grating loop ring -down (FGLRD) Strain sensor Overlap spectrum machine learning algorithm One-dimensional convolutional neural network (1D-CNN)

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Multi Strategy machineenglish Translation System based on machine learning algorithm 5

Multi Strategy Machineenglish Translation System based on Ma...

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5th International Conference on machine learning and machine Intelligence, MLMI 2022

作者： Jiang, Shuang Jilin Animation Institute Jilin Province Changchun China

ISBN: (纸本)9781450397551

The advent of the big data era has brought unprecedented data to machine translation, which is particularly important for data-driven models and algorithms. The degree of public quotation of source text is becoming more and more difficult to understand, but the overall translation performance is improving. On this basis, the multi Strategy machineenglish translation (MSMT) system based on MLA is designed in this paper. Firstly, the MSMT system and machineenglish translation process are analyzed, and the Interactive MSMT system is discussed. Then the neural network translation model of machine learning is proposed. In order to improve the performance of the MSMT system based on MLA proposed in this paper, The results show that in the case of full-text retrieval, the time of English translation is greatly increased, and these time are basically spent in finding and matching these sentences. The sentences that cannot be translated due to the failure of feature index can be translated by the Multi Strategy machine English translation system based on MLA proposed in this paper, which verifies the rationality of the translation system proposed in this paper. © 2022 ACM.

关键词： English Translation machine learning algorithm Multi Strategy machine Translation System Design

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Research on machine learning algorithm-Based Approach for Detecting Abnormal Data from Environmental Sensors in Different Dimensions 23

Research on Machine Learning Algorithm-Based Approach for De...

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Proceedings of the 2023 International Joint Conference on Robotics and Artificial Intelligence

作者： Wenqiang Yue Li-an Chen Haiyan Huang School of Electrical Engineering and Automation Xiamen University of Technology China School of Electrical Engineering and Automation Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology China Xiamen Minghan Electric Co. Ltd. China

ISBN: (纸本)9798400707704

With the development of environmental sensor technology, more and more environmental sensors are widely used in various fields, such as environmental monitoring, smart home, smart city and so on. However, due to the complexity and diversity of environmental sensor data, there are a high number of anomalous data in sensor data, which can lead to misjudgment and false positives, compromising data reliability and accuracy. Therefore, the detection method of abnormal data in different dimensions of environmental sensors based on machine learning algorithms were studied in this paper. Firstly, the environmental sensor data of IBRL (Intel Berkeley Research Lab) data set was preprocessed, including data cleaning, data normalization and feature extraction. Then, the simulation experiment was completed using the data set, and the data of different dimensions was detected using machine learning algorithms such as OneClass Support Vector machine algorithm, Local Outlier Factor algorithm, and Isolation Forest algorithm. Finally, through experimental comparison and analysis of the application effects of the above three algorithms in anomaly detection of environmental data in different dimensions, the Isolation Forest algorithm with the best comprehensive detection effect was finally selected for practical engineering projects. In practical engineering projects, maintenance costs can be effectively reduced, project safety and reliability can be improved, and the purpose of improving project efficiency can ultimately be achieved by the proposed method.

关键词： Abnormal data detection Environmental sensor machine learning algorithm

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Football Player Injury Full-Cycle Management and Monitoring System Based on Blockchain and machine learning algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 41-41页

作者： Pu, Chunwang Zhou, Jun Sun, Jian Zhang, Jianpeng Yuxi Normal Univ Sch Phys Educ Yuxi 653100 Peoples R China Coll Art & Sci Kunming Sch Phys Educ Kunming 650201 Peoples R China Yunnan Agr Univ Sch Phys Educ Kunming 650201 Peoples R China

Football injuries are the most common factor affecting a football player's performance, and the last thing a football player wants. To understand the causes of football players' injuries and how to recover sports injuries most efficiently, the football players' injuries were managed and monitored throughout the whole cycle. However, the traditional football player injury cycle management and monitoring system are not only insecure in data storage, but more importantly, it lacks intelligent analysis of the collected data. With the continuous development of blockchain and machine learning technologies, blockchain technology is used to collect, store, clean, mine and visualize the full-cycle data of football players' injuries, and machine learning is used to provide intelligent solutions for football players' injury recovery. This paper compared the football player's injury full-cycle management and monitoring system based on blockchain and machine learning algorithm with the traditional football player's injury management and monitoring system. The experimental results showed that the average self-processing capacity of the football player injury MMS based on blockchain and ML algorithm was 70%, while the average self-processing capacity of the traditional football player injury management and monitoring system was 50%. Therefore, the application of blockchain and machine learning algorithm in the football player's injury full-cycle management and monitoring system can effectively improve the system's self-processing ability.

关键词： Full-cycle management and supervision system Football player injury machine learning algorithm Blockchain technology Support vector machine

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