Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

Underground Space, Год журнала: 2024, Номер 17, С. 320 - 360

Опубликована: Март 1, 2024

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty that modified SSA proposes adjustment strategy to create a balance between capacity of exploitation and exploration. In AM-SSA, firstly, population initialized cat mapping chaotic sequences improve ergodicity randomness individual sparrow, enhancing global ability. Then individuals are adjusted Tent disturbance Cauchy avoid being too concentrated or scattered, expanding local Finally, producer-scrounger number formula introduced ability seek optimal. addition, it leads improved achieving better accuracy level convergence speed compared original SSA. To demonstrate effectiveness reliability 23 classical benchmark functions 25 IEEE Congress on Evolutionary Computation test (CEC2005), employed as numerical examples investigated comparison some well-known optimization algorithms. statistical results indicate promising performance AM-SSA variety constrained unknown spaces. By utilizing AdaBoost algorithm, multiple sets weak AMSSA-Elman predictor restructured into one strong successive iterations prediction output. Additionally, on-site monitoring data acquired from excavation project Ningbo, China, were selected training testing sample. Meanwhile, predictive outcomes those other different machine learning techniques. end, obtained this real-world geotechnical engineering field reveal feasibility hybrid model, illustrating its power superiority terms computational efficiency, accuracy, stability, robustness. More critically, observing real time daily basis, structural safety associated tunnels could be supervised, which enables decision-makers take concrete control protection measures.

Язык: Английский

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Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses

Advanced Engineering Informatics, Год журнала: 2020, Номер 45, С. 101097 - 101097

Опубликована: Апрель 10, 2020

Язык: Английский

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An AIoT-based system for real-time monitoring of tunnel construction

Tunnelling and Underground Space Technology, Год журнала: 2020, Номер 109, С. 103766 - 103766

Опубликована: Дек. 24, 2020

Язык: Английский

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Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2021, Номер 13(6), С. 1500 - 1512

Опубликована: Сен. 14, 2021

A novel and effective artificial neural network (ANN) optimized using differential evolution (DE) is first introduced to provide a robust reliable forecasting of jet grouted column diameters. The proposed computational method adopts the DE algorithm tackle difficulties in training performance networks optimize four quintessential hyper-parameters (i.e. epoch size, number neurons hidden layer, layers, regularization parameter) that govern efficacy. This approach further enhanced by stochastic gradient optimization allow 'expensive' computation efforts. ANN-DE trained prepared grouting dataset, then verified compared with prevalent machine learning tools, i.e. support vector (SVM). results show that, outperforms existing methods for predicting diameter columns since it well balances efficiency model performance. Specifically, achieved root mean square error (RMSE) values 0.90603 0.92813 testing phases, respectively. corresponding were 0.8905 0.9006 ANN, then, 0.87569 0.89968 SVM, paradigm bound be useful solving various geotechnical engineering problems regardless multi-dimension nonlinearity.

Язык: Английский

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Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images

Energy, Год журнала: 2021, Номер 240, С. 122599 - 122599

Опубликована: Ноя. 19, 2021

Язык: Английский

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