Determining the weights of influencing factors of construction lands with a neural network algorithm: a case study based on Ya’an City

Gao, Lei; Zhou, Yazhou; Guo, Kairui; Huang, Yong; Zhu, Xiaofan

doi:10.1007/s12145-021-00657-8

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…In the interactive mechanism teaching mode, there are four interaction modes between students, teachers, and teaching materials and web learning resources, between students, teachers, and students, inside and outside the classroom, and between students, teachers, and synchronous or asynchronous media. e interaction types include content, interpersonal, time and space, and media [20][21][22].…”

Section: Construction Of Interactive Mechanism Teaching Modementioning

confidence: 99%

Research on College English Teaching Evaluation Based on Neural Network

Zhao

2022

Mathematical Problems in Engineering

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Aiming at the difficulty in adapting the traditional English teaching evaluation model to the new situation of current education development, a research on college English teaching evaluation based on neural network is proposed. The thesis proposes that based on the neural network, from the perspective of teachers and students, while creating a positive and harmonious learning atmosphere, a college English teaching environment with interactive mechanism is established, and a teaching mode of English interactive mechanism is constructed in it. To obtain a better neural network model, to establish the performance evaluation index of college English teaching, to calculate the weight of the performance evaluation index of English teaching, and to realize the evaluation of college English teaching based on neural network, the experimental results show that the use of this method can significantly improve students’ English scores, and the method has high approximation accuracy.

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Section: Construction Of Interactive Mechanism Teaching Modementioning

confidence: 99%

Research on College English Teaching Evaluation Based on Neural Network

Zhao

2022

Mathematical Problems in Engineering

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“…In this section, we revise the preliminary comprehensive prediction model and the revised and optimized comprehensive prediction model is established to predict the settlement of the rock-soil foundation of the building. An engineering example is provided for verification of the comprehensive prediction model [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Discrete-Time Model-Based Method for Predicting Settlement of Geotechnical Foundations in Buildings

Yang

Jiang

2022

Mobile Information Systems

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The selection of a single prediction method is difficult to adapt to the actual engineering situation in the construction geotechnical foundation’s settlement prediction. This article proposes a modified optimized comprehensive prediction model to independently mine the construction geotechnical foundation settlement monitoring data from different angles. The proposed model analyzes the construction geotechnical foundation settlement’s change law and realizes the comprehensive prediction of construction geotechnical foundation settlement. Initially, the preliminary integrated discrete-time prediction model is established based on the combination of the hyperbolic method and the GM (1, 1) model’s modeling mechanism. The GM (1, 1) modeling mechanism is based on the idea of arithmetic weighted average combination. Then the real-time correction weight coefficients are constructed and the real-time correction amount is calculated to modify the initial integrated prediction model. The modified optimized integrated prediction model is established too. Finally, the modified optimized integrated prediction model is utilized to predict the settlement of building geotechnical foundations. The experimental results of A-Ma city project, China show that the modified optimized integrated prediction model has better prediction accuracy and has universal applicability than the hyperbolic method and GM (1, 1) in the prediction of foundation settlement of building geotechnical foundations. It can effectively reduce the prediction error after the combination of single methods.

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