Accesso libero

Real-time monitoring and deep learning prediction modeling of rainfall infiltration effects in slope stability risk assessment

Guopeng Qu

Guopeng Qu

School of Architectural Construction, Jiangsu Vocational Institute of Architectural TechnologyXuzhou, China
Cerca questo autore su
Sciendo|Google Scholar
Qu, Guopeng
 e 
Kan Wu

Kan Wu

School of Environment and Spatial Informatics, China University of Mining and TechnologyXuzhou, China
Cerca questo autore su
Sciendo|Google Scholar
Wu, Kan
  
26 set 2025
Real-time monitoring and deep learning prediction modeling of rainfall infiltration effects in slope stability risk assessment's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Pubblicato online: 26 set 2025
Ricevuto: 19 gen 2025
Accettato: 08 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1042
Parole chiave
© 2025 Guopeng Qu and Kan Wu, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Schematic diagram of typical water content distribution profile
Schematic diagram of typical water content distribution profile

Figure 2.

The change of pore water pressure in different rainfall time
The change of pore water pressure in different rainfall time

Figure 3.

X direction displacement in different rainfall time
X direction displacement in different rainfall time

Figure 4.

The z-direction displacement in different rainfall time
The z-direction displacement in different rainfall time

Figure 5.

Deformation velocity of x direction under different rainfall time
Deformation velocity of x direction under different rainfall time

Figure 6.

Deformation velocity of z direction in different rainfall time
Deformation velocity of z direction in different rainfall time

Figure 7.

Convergence of three parallel network training
Convergence of three parallel network training

The orthogonal design factors and levels of the stability analysis are used

Influencing factor Level
1 2 3 4
Solar rainfall (mm) 100 200 300 400
Heavy (kN/m3) 17.31 19.04 19.62 20.18
Cohesive (kPa) 7 10 13 16
Internal friction Angle 15 18 20 22
Saturation coefficient(m/hr) 0.007 0.014 0.018 0.022
Saturated volume moisture content(%) 32 36 42 46
Crack depth (m) 0.5 1 1.5 2
POE high (m) 15 30 45 60
Slope ratio 1:1.2 1:1.6 1:1.8 1:2

The long slope open-dew parameters used in the analysis of stability

Severity (kN/m3) Cohesion (kPa) Interior friction Angle Saturation coefficient(m/hr) Saturated volume moisture content(%)
Same as the weathering layer 16.73 21.64 1.9×10-4 Same as the weathering layer

Training sample

Serial number Solar rainfall (mm) Heavy (kN/m3) Cohesive (kPa) Internal friction Angle Saturation coefficient (m/hr) Saturated volume moisture content (%) Crack depth (m) POE high (m)
1 90.2 17.49 8 19.51 0.0197 39.49 1.12 12.38
2 129.98 18.24 7.35 12.42 0.0151 35.44 0.84 27.72
3 82.6 19.58 9.75 17.27 0.0178 38.99 1.24 28.85
4 116.97 19.25 7.77 7.25 0.0222 38.31 1.13 11.39
5 86.01 15.87 8.59 21.07 0.0155 36.07 1.32 29.78
6 97.59 17.5 8.74 14.55 0.016 38.94 1.18 32.94
7 97.47 20.42 8.73 16.85 0.0112 29.64 1.41 29.97
8 100.16 22.07 8.41 10.73 0.0159 37.48 1.14 25.69
9 106.46 21.97 9.45 14.98 0.0048 34.92 1.39 24.22
10 66.79 16.76 9.44 14.51 0.0131 34.14 1.42 28.87
11 108.81 15.53 7.78 15.5 0.0144 38.53 1.19 22.37
12 112.55 14.54 9.55 13.91 0.0117 30.55 1.49 8.59
13 74.15 14.66 6.61 18.85 0.0125 32.1 1 24.29
14 118.54 13.84 8.87 15.93 0.0123 35.94 1.4 12.48
15 126.51 16.78 9.33 17.18 0.0111 39.56 1.16 21.68
16 85.62 19.62 7.83 15.85 0.0122 33.49 1.33 25.66
17 103.12 17.27 11.42 15.72 0.0137 34.36 1.39 37.05
18 93.1 16.85 7.31 19.34 0.023 37.04 1.32 20.32
19 104.98 16.62 7.9 16 0.0119 36.74 1.22 19.32
20 90.94 20.59 7.49 16.06 0.0173 40.67 1.42 26.07
21 97.5 14.73 5.25 17.06 0.0168 41.39 1.29 29.27
22 112.17 15.97 8.9 17.62 0.0219 33.95 1.4 24.37
23 108.86 17.46 8.92 14.16 0.0132 34.26 1.12 33.65
24 93.44 19.02 9.92 18.15 0.0164 37.54 1.45 17.35
25 104.8 17.32 7.58 16.95 0.0184 36.09 1.21 15.36
26 84.1 16.72 9.21 11.24 0.015 37.37 1.32 36.66
27 120.65 15.51 10.29 14.99 0.0136 40.14 0.94 27.44
28 118.06 19.82 5.87 20.39 0.0141 35.17 1.15 22.84
29 108.23 18.02 9.4 15.31 0.0121 39.23 1.06 27.27
30 113.38 20.08 10.84 13.75 0.0128 26.09 1.25 16.44
31 101.19 15.93 7.03 13.41 0.017 35.65 1.34 33.17
32 102.99 16.42 7.61 15.28 0.0049 34.91 1.23 10.22

The prediction of the model in a stable state

Serial number Solar rainfall (mm) Heavy (kN/m3) Cohesive (kPa) Internal friction Angle Saturation coefficient (m/hr) Saturated volume moisture content (%) Crack depth (m) POE high (m)
1 90.453 18.822 8.609 19.818 0.01984 40.866 1.1408 13.0754
2 130.233 19.572 7.959 12.728 0.01524 36.816 0.8608 28.4154
3 82.853 20.912 10.359 17.578 0.01794 40.366 1.2608 29.5454
4 117.223 20.582 8.379 7.558 0.02234 39.686 1.1508 12.0854
5 86.263 17.202 9.199 21.378 0.01564 37.446 1.3408 30.4754

The results of the model in the test library

Serial number Solar rainfall (mm) Heavy (kN/m3) Cohesive (kPa) Internal friction Angle Saturation coefficient (m/hr) Saturated volume moisture content (%) Crack depth (m) POE high (m)
1 90.43 18.61 8.59 19.79 0.01982 40.75 1.138 13.0742
2 130.21 19.36 7.94 12.7 0.01522 36.7 0.858 28.4142
3 82.83 20.7 10.34 17.55 0.01792 40.25 1.258 29.5442
4 117.2 20.37 8.36 7.53 0.02232 39.57 1.148 12.0842
5 86.24 16.99 9.18 21.35 0.01562 37.33 1.338 30.4742

The experimental results in the calculation of the model

Serial number Rainfall duration(h) Lift up grade Cohesive force Internal friction Saturated volume moisture content(%) Crack depth (m) POE high (m)
0 0 20 40 10.04 15.26 35.21 1.418 25.064
1 8 20 40 10.03 14.79 35.52 1.138 34.344
2 16 20 40 10.37 15.78 38.8 1.468 18.412
3 24 20 40 10.14 14.19 37.35 1.228 16.232
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
Feed RSS della rivista