In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlem...
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In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlement and may cause potential safety hazards to the surrounding existing buildings, directly affecting the safety of urban operation. When simulating and predicting surface settlements, the small-strain soil hardening model can more accurately characterize the mechanical parameters of soil. Nevertheless, its parameters are numerous and complicated to determine accurately, so parameter inversion is needed to determine the accurate parameters of the soft soil layer in order to more accurately predict the surface settlement. This study uses the EFAST method to analyse the sensitivity of the HSS model parameters of soft soil strata. It is determined that the parameters that have the most significant impact on the surface settlement are the reference tangent modulus, rebound modulus, and effective cohesion. Then, XGBoost's fast calculation speed and high precision of SSA inversion are used to inverse and optimize the parameters with high sensitivity. Finally, according to the parameters of the soft soil layer obtained from inversion and measured data, the settlement deformation and safety behaviour of existing buildings are analysed. Combined with the actual shield tunnel project in a city along a river, the inversion calculation shows that the overall average error of the transverse monitoring section is 1.04 mm, and the average maximum error of each monitoring point in the overall shield process is 2.87 mm. The prediction effect is significantly improved compared with the original parameters. The accuracy of the inversion of soil layer parameters is verified from the perspective of time and space. The average settlement of the river embankment foundation is 2.5 mm. Compared with the original parameter data, the prediction results have been greatly improved,
With the regular development of the global epidemic, the global port shipping supply is tight. The problem of port congestion, soaring freight rates, and hard-to-find container space has emerged. This paper proposes a...
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With the regular development of the global epidemic, the global port shipping supply is tight. The problem of port congestion, soaring freight rates, and hard-to-find container space has emerged. This paper proposes a new joint berth-quay crane allocation model, namely E-B&QC, by taking the minimum of the time in the port of the ship, the cost of extra transportation distance for collector trucks in the land area of the port, and the cost of extra waiting time for ships. Then, the deficiencies of the sparrow search algorithm (SSA) are considered in solving the E-B&QC model, and the SSA is improved based on the three-dimensional Cat chaos mapping and quantum computing theory. Chaotic Quantum sparrow Search algorithm (CQSSA) is proposed, population individual coding rules are formulated, also E-B&QC model solving algorithm is established. Finally, a new berth-crane allocation optimization method, namely, E-B&QC-CQSSA, is proposed. Subsequently, the feasibility and superiority of the proposed allocation model and solution algorithm are tested according to the actual data of a small river port in the south and a medium-sized river port in the north. Simulation examples show that the E-B&QC model can develop different high-quality solutions for container ports under different working conditions, and the more complex the actual situation of the port, the more significant the optimization effect. The proposed CQSSA for E-B&QC model can obtain a better solution.
In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper propos...
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In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data;on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments;the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.
In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)-sparrow search algorithm (SSA)-support vector regression machine (SVR)-kernel density estimation...
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In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)-sparrow search algorithm (SSA)-support vector regression machine (SVR)-kernel density estimation (KDE) model to forecast China's future energy consumption in 2022-2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions.
One of the prerequisites for the stable operation of the power system is to ensure the transient stability of the power system. At present, many intelligent algorithms are applied to the transient stability assessment...
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One of the prerequisites for the stable operation of the power system is to ensure the transient stability of the power system. At present, many intelligent algorithms are applied to the transient stability assessment of power systems, but there are still some problems, such as poor effectiveness and low accuracy due to huge data. Aiming at these problems, this paper uses the information entropy-based rough set for attribute dimensionality reduction, filters unnecessary attributes, and obtains a simplified data set. Since the prediction accuracy of the traditional extreme learning machine is not very high, this paper adopts the improved sparrow algorithm to optimize the extreme learning machine, and obtains a high accuracy. Finally, the IEEE39 system simulation results show that the method proposed in this paper can effectively reduce the data dimension, and can quickly and accurately discriminate the transient and stable state of the power system.
The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition numbe...
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The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition number K and penalty factor of variational mode decomposition (VMD) by using the sparrow intelligent algorithm, decomposes the power sequence in a power mode by using the optimised VMD decomposition method and sends all sub-components to a long short-term memory (LSTM) network for prediction.
To address the issue of the low utilization rate of mold platforms in the production line of precast concrete components, a method combining an improved sparrow algorithm with the lowest horizontal line algorithm is p...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
To address the issue of the low utilization rate of mold platforms in the production line of precast concrete components, a method combining an improved sparrow algorithm with the lowest horizontal line algorithm is proposed. Firstly, a layout model for precast concrete components considering the reinforcement process is established. Subsequently, the improved sparrow algorithm is employed to optimize the production sequence of components, and the lowest horizontal line algorithm is utilized to determine the optimized layout positions of the components. By introducing a cubic chaotic sequence to initialize the sparrow population, setting a similarity mutation operator, and applying the cauchy mutation to similar individuals, the sparrow algorithm is enhanced. This enhancement ensures a more even distribution of the sparrow population in the solution space, thereby strengthening the global optimization capability of the sparrow algorithm. Finally, the effectiveness of the proposed method is validated through simulation tests.
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