<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Geotechnical investigation is an important aspect of construction engineering. In order to optimize this link, this paper analyzes the current status of the application of geotechnical investigation in construction engineering through the fusion of multidimensional spatial data interpolation of geotechnical geology as well as the structural design of geotechnical profiles by combining with triangular mesh search algorithm. On this basis, the parameter prediction of the fusion of multi-source parameter data in geotechnicals was carried out by determining the estimated variance and weight coefficients. In addition, the optimal interpolation algorithm was selected through the indoor test and in-situ test, and the accuracy of the modeling of the soil and the prediction of the geotechnical parameters were analyzed. The results show that the modeling width error is between [0.04,0.20], the length error is between [0.7,0.23], the planar accuracy error is around 0.2cm, the depth error is within 0.02cm, and the errors of the actual and predicted values of the rock and map parameters are within 0.1 and 0.09, respectively. Geotechnical investigation measures and techniques in construction engineering based on multi-source data fusion can optimize construction engineering.</p>
</abstract>

Geotechnical investigation is an important aspect of construction engineering. In order to optimize this link, this paper analyzes the current status of the application of geotechnical investigation in construction engineering through the fusion of multidimensional spatial data interpolation of geotechnical geology as well as the structural design of geotechnical profiles by combining with triangular mesh search algorithm. On this basis, the parameter prediction of the fusion of multi-source parameter data in geotechnicals was carried out by determining the estimated variance and weight coefficients. In addition, the optimal interpolation algorithm was selected through the indoor test and in-situ test, and the accuracy of the modeling of the soil and the prediction of the geotechnical parameters were analyzed. The results show that the modeling width error is between [0.04,0.20], the length error is between [0.7,0.23], the planar accuracy error is around 0.2cm, the depth error is within 0.02cm, and the errors of the actual and predicted values of the rock and map parameters are within 0.1 and 0.09, respectively. Geotechnical investigation measures and techniques in construction engineering based on multi-source data fusion can optimize construction engineering.

Geotechnical investigation measures and techniques in construction engineering based on multi-source data fusion

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Geotechnical investigation is an important aspect of construction engineering. In order to optimize this link, this paper analyzes the current status of the...

Geotechnical investigation measures and techniques in construction engineering based on multi-source data fusion

Data publikacji: 15 lis 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 26 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01155

Słowa kluczowe
Spatial data interpolation, Multi-source data fusion, Triangular mesh search, Parameter prediction, Geotechnical survey

© 2023 Hanrui Pei, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Geotechnical investigation measures and techniques in construction engineering based on multi-source data fusion

Data publikacji: 15 lis 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 26 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01155

Słowa kluczoweSpatial data interpolation, Multi-source data fusion, Triangular mesh search, Parameter prediction, Geotechnical survey

© 2023 Hanrui Pei, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Spatial data interpolation, Multi-source data fusion, Triangular mesh search, Parameter prediction, Geotechnical survey