A New Bayesian Network Model for the Risk Assessment of Water Inrush in Karst Tunnels

<table class="table-group" id="tab14"><tr><td><table class="table"><tr><td class="thead-hr" colspan="9"><hr/></td></tr><tr class="thead"><td class="align_left"></td><td class="align_center">Strong</td><td class="align_center">Medium 1</td><td class="align_center">Medium 2</td><td class="align_center">Medium 3</td><td class="align_center">Weak 1</td><td class="align_center">Weak 2</td><td class="align_center">None 1</td><td class="align_center">None 2</td></tr><tr><td class="thead-hr" colspan="9"><hr/></td></tr><tr><td class="align_center"><span style="width: 15.5881ptpx;"><svg height="11.8174pt" id="M93" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 15.5881 11.8174" width="15.5881pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.071,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.503,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g></svg></span></td><td class="align_center">I</td><td class="align_center">II-1</td><td class="align_center">II-2</td><td class="align_center">II-3</td><td class="align_center">III-1</td><td class="align_center">III-2</td><td class="align_center">IV</td><td class="align_center">V</td></tr><tr class="table-tr"><td colspan="9"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td><div>The water inrush quantity is divided into 8 sections corresponding to the subgrading of water irruption quantity in the previous paper.<br/></div></td></tr></table>

<div><svg height="11.8174pt" id="M91" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 15.5881 11.8174" width="15.5881pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.071,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.503,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"></path></g></svg>–<svg height="11.8174pt" id="M92" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 15.5881 11.8174" width="15.5881pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.071,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.503,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g></svg> grading interval.</div>

Geofluids

tab14

Table 14

Table 14: A New Bayesian Network Model for the Risk Assessment of Water Inrush in Karst Tunnels 