SEISMOLOGY AND GEOLOGY ›› 2024, Vol. 46 ›› Issue (4): 934-954.DOI: 10.3969/j.issn.0253-4967.2024.04.010
• Research paper • Previous Articles Next Articles
LI Zhi-heng1,2)(), XIE Jun-ju1),*(), LI Ke-wei1,3), WEN Zeng-ping1), LI Xiao-jun4), WANG Zhi-cai2), XU Hong-tai2), ZHAO Xiao-fen1), ZHANG Na1)
Received:
2023-07-06
Revised:
2023-12-09
Online:
2024-08-20
Published:
2024-09-23
Contact:
XIE Jun-ju
李志恒1,2)(), 谢俊举1),*(), 李柯苇1,3), 温增平1), 李小军4), 王志才2), 许洪泰2), 赵晓芬1), 张娜1)
通讯作者:
谢俊举
作者简介:
李志恒, 男, 1990年生, 2018年于中国地质大学(北京)获地质工程专业硕士学位, 工程师, 主要从事工程地震方面的研究, E-mail: leezh87@163.com。
基金资助:
LI Zhi-heng, XIE Jun-ju, LI Ke-wei, WEN Zeng-ping, LI Xiao-jun, WANG Zhi-cai, XU Hong-tai, ZHAO Xiao-fen, ZHANG Na. EMPIRICAL EXTRAPOLATION MODEL OF SITE SHEAR WAVE VELOCITY AND ITS APPLICABILITY IN SHANDONG PROVINCE[J]. SEISMOLOGY AND GEOLOGY, 2024, 46(4): 934-954.
李志恒, 谢俊举, 李柯苇, 温增平, 李小军, 王志才, 许洪泰, 赵晓芬, 张娜. 山东地区场地剪切波速经验外推模型及其适用性[J]. 地震地质, 2024, 46(4): 934-954.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.dzdz.ac.cn/EN/10.3969/j.issn.0253-4967.2024.04.010
深度/m | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|
6 | 0.9232 | 1.0307 |
7 | 0.9395 | 0.8845 |
8 | 0.9610 | 0.7152 |
9 | 0.9739 | 0.5996 |
10 | 0.9820 | 0.4879 |
11 | 0.9878 | 0.3932 |
12 | 0.9902 | 0.3133 |
13 | 0.9937 | 0.2393 |
14 | 0.9952 | 0.1822 |
15 | 0.9979 | 0.1260 |
16 | 0.9984 | 0.0833 |
17 | 0.9994 | 0.0481 |
18 | 0.9998 | 0.0222 |
19 | 1.0000 | 0.0081 |
Table1 Correlation coefficient and standard deviation of prediction error between VS20est. and VS20 obtained based on the constant velocity extrapolation method
深度/m | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|
6 | 0.9232 | 1.0307 |
7 | 0.9395 | 0.8845 |
8 | 0.9610 | 0.7152 |
9 | 0.9739 | 0.5996 |
10 | 0.9820 | 0.4879 |
11 | 0.9878 | 0.3932 |
12 | 0.9902 | 0.3133 |
13 | 0.9937 | 0.2393 |
14 | 0.9952 | 0.1822 |
15 | 0.9979 | 0.1260 |
16 | 0.9984 | 0.0833 |
17 | 0.9994 | 0.0481 |
18 | 0.9998 | 0.0222 |
19 | 1.0000 | 0.0081 |
深度/m | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|
6 | 0.8021 | 1.7491 |
7 | 0.8438 | 1.5863 |
8 | 0.8820 | 1.4086 |
9 | 0.9113 | 1.2879 |
10 | 0.9335 | 1.1414 |
11 | 0.9456 | 1.0356 |
12 | 0.9484 | 0.9256 |
13 | 0.9550 | 0.8221 |
14 | 0.9576 | 0.7308 |
15 | 0.9721 | 0.6250 |
16 | 0.9757 | 0.5381 |
17 | 0.9806 | 0.4649 |
18 | 0.9853 | 0.3826 |
19 | 0.9899 | 0.3286 |
20 | 0.9922 | 0.2650 |
21 | 0.9937 | 0.2017 |
22 | 0.9960 | 0.1508 |
23 | 0.9974 | 0.1098 |
24 | 0.9981 | 0.0793 |
25 | 0.9989 | 0.0557 |
26 | 0.9993 | 0.0372 |
27 | 0.9997 | 0.0223 |
28 | 0.9999 | 0.0127 |
29 | 1.0000 | 0.0044 |
Table2 Correlation coefficient and standard deviation of prediction error between VS30est. and VS30 obtained using the constant velocity extrapolation method
深度/m | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|
6 | 0.8021 | 1.7491 |
7 | 0.8438 | 1.5863 |
8 | 0.8820 | 1.4086 |
9 | 0.9113 | 1.2879 |
10 | 0.9335 | 1.1414 |
11 | 0.9456 | 1.0356 |
12 | 0.9484 | 0.9256 |
13 | 0.9550 | 0.8221 |
14 | 0.9576 | 0.7308 |
15 | 0.9721 | 0.6250 |
16 | 0.9757 | 0.5381 |
17 | 0.9806 | 0.4649 |
18 | 0.9853 | 0.3826 |
19 | 0.9899 | 0.3286 |
20 | 0.9922 | 0.2650 |
21 | 0.9937 | 0.2017 |
22 | 0.9960 | 0.1508 |
23 | 0.9974 | 0.1098 |
24 | 0.9981 | 0.0793 |
25 | 0.9989 | 0.0557 |
26 | 0.9993 | 0.0372 |
27 | 0.9997 | 0.0223 |
28 | 0.9999 | 0.0127 |
29 | 1.0000 | 0.0044 |
深度/m | a0 | a1 | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|---|---|
6 | -0.4236 | 1.2317 | 0.8450 | 0.0533 |
7 | -0.4369 | 1.2336 | 0.8811 | 0.0472 |
8 | -0.4069 | 1.2160 | 0.9068 | 0.0420 |
9 | -0.3742 | 1.1976 | 0.9274 | 0.0373 |
10 | -0.3388 | 1.1781 | 0.9441 | 0.0329 |
11 | -0.2978 | 1.1565 | 0.9574 | 0.0288 |
12 | -0.2583 | 1.1357 | 0.9677 | 0.0251 |
13 | -0.2228 | 1.1168 | 0.9762 | 0.0216 |
14 | -0.1839 | 1.0968 | 0.9840 | 0.0178 |
15 | -0.1542 | 1.0807 | 0.9894 | 0.0145 |
16 | -0.1254 | 1.0652 | 0.9934 | 0.0114 |
17 | -0.0937 | 1.0486 | 0.9964 | 0.0084 |
18 | -0.0626 | 1.0323 | 0.9984 | 0.0057 |
19 | -0.0306 | 1.0158 | 0.9996 | 0.0028 |
Table3 Regression analysis of the predictive empirical VS20 model based on the velocity gradient method in Shandong province
深度/m | a0 | a1 | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|---|---|
6 | -0.4236 | 1.2317 | 0.8450 | 0.0533 |
7 | -0.4369 | 1.2336 | 0.8811 | 0.0472 |
8 | -0.4069 | 1.2160 | 0.9068 | 0.0420 |
9 | -0.3742 | 1.1976 | 0.9274 | 0.0373 |
10 | -0.3388 | 1.1781 | 0.9441 | 0.0329 |
11 | -0.2978 | 1.1565 | 0.9574 | 0.0288 |
12 | -0.2583 | 1.1357 | 0.9677 | 0.0251 |
13 | -0.2228 | 1.1168 | 0.9762 | 0.0216 |
14 | -0.1839 | 1.0968 | 0.9840 | 0.0178 |
15 | -0.1542 | 1.0807 | 0.9894 | 0.0145 |
16 | -0.1254 | 1.0652 | 0.9934 | 0.0114 |
17 | -0.0937 | 1.0486 | 0.9964 | 0.0084 |
18 | -0.0626 | 1.0323 | 0.9984 | 0.0057 |
19 | -0.0306 | 1.0158 | 0.9996 | 0.0028 |
深度/m | a0 | a1 | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|---|---|
6 | -0.0249 | 1.0681 | 0.7233 | 0.0516 |
7 | -0.1169 | 1.1065 | 0.7668 | 0.0479 |
8 | -0.1767 | 1.1301 | 0.8047 | 0.0443 |
9 | -0.2202 | 1.1465 | 0.8370 | 0.0409 |
10 | -0.2400 | 1.1523 | 0.8649 | 0.0375 |
11 | -0.2385 | 1.1487 | 0.8874 | 0.0344 |
12 | -0.2227 | 1.1389 | 0.9043 | 0.0319 |
13 | -0.2034 | 1.1277 | 0.9181 | 0.0296 |
14 | -0.1706 | 1.1107 | 0.9300 | 0.0275 |
15 | -0.1605 | 1.1035 | 0.9409 | 0.0253 |
16 | -0.1493 | 1.0960 | 0.9499 | 0.0234 |
17 | -0.1360 | 1.0877 | 0.9578 | 0.0215 |
18 | -0.1222 | 1.0791 | 0.9648 | 0.0196 |
19 | -0.1101 | 1.0715 | 0.9716 | 0.0177 |
20 | -0.1005 | 1.0649 | 0.9774 | 0.0158 |
21 | -0.0935 | 1.0594 | 0.9824 | 0.0139 |
22 | -0.0858 | 1.0537 | 0.9866 | 0.0122 |
23 | -0.0800 | 1.0489 | 0.9900 | 0.0106 |
24 | -0.0723 | 1.0433 | 0.9927 | 0.0090 |
25 | -0.0632 | 1.0372 | 0.9950 | 0.0074 |
26 | -0.0530 | 1.0307 | 0.9969 | 0.0059 |
27 | -0.0416 | 1.0237 | 0.9983 | 0.0044 |
28 | -0.0291 | 1.0163 | 0.9992 | 0.0029 |
29 | -0.0155 | 1.0085 | 0.9998 | 0.0015 |
Table4 Results of regression analysis of VS30 empirical extrapolation relationships in Shandong province based on the velocity gradient method
深度/m | a0 | a1 | 皮尔逊相关系数r | 预测误差标准差σRES |
---|---|---|---|---|
6 | -0.0249 | 1.0681 | 0.7233 | 0.0516 |
7 | -0.1169 | 1.1065 | 0.7668 | 0.0479 |
8 | -0.1767 | 1.1301 | 0.8047 | 0.0443 |
9 | -0.2202 | 1.1465 | 0.8370 | 0.0409 |
10 | -0.2400 | 1.1523 | 0.8649 | 0.0375 |
11 | -0.2385 | 1.1487 | 0.8874 | 0.0344 |
12 | -0.2227 | 1.1389 | 0.9043 | 0.0319 |
13 | -0.2034 | 1.1277 | 0.9181 | 0.0296 |
14 | -0.1706 | 1.1107 | 0.9300 | 0.0275 |
15 | -0.1605 | 1.1035 | 0.9409 | 0.0253 |
16 | -0.1493 | 1.0960 | 0.9499 | 0.0234 |
17 | -0.1360 | 1.0877 | 0.9578 | 0.0215 |
18 | -0.1222 | 1.0791 | 0.9648 | 0.0196 |
19 | -0.1101 | 1.0715 | 0.9716 | 0.0177 |
20 | -0.1005 | 1.0649 | 0.9774 | 0.0158 |
21 | -0.0935 | 1.0594 | 0.9824 | 0.0139 |
22 | -0.0858 | 1.0537 | 0.9866 | 0.0122 |
23 | -0.0800 | 1.0489 | 0.9900 | 0.0106 |
24 | -0.0723 | 1.0433 | 0.9927 | 0.0090 |
25 | -0.0632 | 1.0372 | 0.9950 | 0.0074 |
26 | -0.0530 | 1.0307 | 0.9969 | 0.0059 |
27 | -0.0416 | 1.0237 | 0.9983 | 0.0044 |
28 | -0.0291 | 1.0163 | 0.9992 | 0.0029 |
29 | -0.0155 | 1.0085 | 0.9998 | 0.0015 |
深度/m | b0 | b1 | r | σRES |
---|---|---|---|---|
6 | 0.0115 | 1.0400 | 0.8397 | 0.0706 |
7 | 0.2043 | 0.9533 | 0.8293 | 0.0746 |
8 | 0.0517 | 1.0146 | 0.8822 | 0.0643 |
9 | -0.0085 | 1.0376 | 0.9035 | 0.0598 |
10 | -0.0114 | 1.0355 | 0.9186 | 0.0563 |
11 | 0.0096 | 1.0238 | 0.9293 | 0.0536 |
12 | 0.0215 | 1.0163 | 0.9301 | 0.0543 |
13 | -0.0148 | 1.0285 | 0.9456 | 0.0490 |
14 | 0.1002 | 0.9785 | 0.9295 | 0.0564 |
15 | -0.0640 | 1.0432 | 0.9662 | 0.0401 |
16 | -0.0700 | 1.0428 | 0.9669 | 0.0406 |
17 | -0.0417 | 1.0282 | 0.9774 | 0.0343 |
18 | -0.0501 | 1.0283 | 0.9790 | 0.0338 |
19 | -0.0097 | 1.0100 | 0.9842 | 0.0299 |
Table5 Regression coefficients, correlation coefficients and standard deviations of prediction errors between VS(z)—VS[z,20] calculated based on conditional independence models
深度/m | b0 | b1 | r | σRES |
---|---|---|---|---|
6 | 0.0115 | 1.0400 | 0.8397 | 0.0706 |
7 | 0.2043 | 0.9533 | 0.8293 | 0.0746 |
8 | 0.0517 | 1.0146 | 0.8822 | 0.0643 |
9 | -0.0085 | 1.0376 | 0.9035 | 0.0598 |
10 | -0.0114 | 1.0355 | 0.9186 | 0.0563 |
11 | 0.0096 | 1.0238 | 0.9293 | 0.0536 |
12 | 0.0215 | 1.0163 | 0.9301 | 0.0543 |
13 | -0.0148 | 1.0285 | 0.9456 | 0.0490 |
14 | 0.1002 | 0.9785 | 0.9295 | 0.0564 |
15 | -0.0640 | 1.0432 | 0.9662 | 0.0401 |
16 | -0.0700 | 1.0428 | 0.9669 | 0.0406 |
17 | -0.0417 | 1.0282 | 0.9774 | 0.0343 |
18 | -0.0501 | 1.0283 | 0.9790 | 0.0338 |
19 | -0.0097 | 1.0100 | 0.9842 | 0.0299 |
深度/m | b0 | b1 | r | σRES |
---|---|---|---|---|
6 | 0.2134 | 0.9649 | 0.7095 | 0.0641 |
7 | 0.3001 | 0.9242 | 0.7545 | 0.0611 |
8 | 0.2353 | 0.9492 | 0.8041 | 0.0564 |
9 | 0.1656 | 0.9778 | 0.8361 | 0.0530 |
10 | 0.2183 | 0.9520 | 0.8606 | 0.0499 |
11 | 0.2574 | 0.9337 | 0.8720 | 0.0485 |
12 | 0.3053 | 0.9113 | 0.8637 | 0.0506 |
13 | 0.3223 | 0.9023 | 0.8653 | 0.0509 |
14 | 0.5652 | 0.7979 | 0.8123 | 0.0598 |
15 | 0.2208 | 0.9412 | 0.8939 | 0.0465 |
16 | 0.2122 | 0.9428 | 0.8917 | 0.0475 |
17 | 0.2290 | 0.9341 | 0.8976 | 0.0468 |
18 | 0.2287 | 0.9318 | 0.9076 | 0.0450 |
19 | 0.2348 | 0.9281 | 0.9236 | 0.0414 |
20 | 0.1849 | 0.9464 | 0.9298 | 0.0401 |
21 | 0.1626 | 0.9525 | 0.9325 | 0.0397 |
22 | 0.0983 | 0.9762 | 0.9447 | 0.0364 |
23 | 0.0066 | 1.0110 | 0.9508 | 0.0349 |
24 | 0.0262 | 1.0010 | 0.9516 | 0.0351 |
25 | 0.0146 | 1.0040 | 0.9575 | 0.0335 |
26 | 0.0209 | 0.9999 | 0.9607 | 0.0327 |
27 | 0.0178 | 0.9996 | 0.9674 | 0.0303 |
28 | 0.0173 | 0.9988 | 0.9717 | 0.0287 |
29 | -0.0434 | 1.0215 | 0.9744 | 0.0282 |
Table6 Regression coefficients, correlation coefficients and standard deviations of prediction errors between VS(z)—VS[z,30] calculated based on conditional independence models
深度/m | b0 | b1 | r | σRES |
---|---|---|---|---|
6 | 0.2134 | 0.9649 | 0.7095 | 0.0641 |
7 | 0.3001 | 0.9242 | 0.7545 | 0.0611 |
8 | 0.2353 | 0.9492 | 0.8041 | 0.0564 |
9 | 0.1656 | 0.9778 | 0.8361 | 0.0530 |
10 | 0.2183 | 0.9520 | 0.8606 | 0.0499 |
11 | 0.2574 | 0.9337 | 0.8720 | 0.0485 |
12 | 0.3053 | 0.9113 | 0.8637 | 0.0506 |
13 | 0.3223 | 0.9023 | 0.8653 | 0.0509 |
14 | 0.5652 | 0.7979 | 0.8123 | 0.0598 |
15 | 0.2208 | 0.9412 | 0.8939 | 0.0465 |
16 | 0.2122 | 0.9428 | 0.8917 | 0.0475 |
17 | 0.2290 | 0.9341 | 0.8976 | 0.0468 |
18 | 0.2287 | 0.9318 | 0.9076 | 0.0450 |
19 | 0.2348 | 0.9281 | 0.9236 | 0.0414 |
20 | 0.1849 | 0.9464 | 0.9298 | 0.0401 |
21 | 0.1626 | 0.9525 | 0.9325 | 0.0397 |
22 | 0.0983 | 0.9762 | 0.9447 | 0.0364 |
23 | 0.0066 | 1.0110 | 0.9508 | 0.0349 |
24 | 0.0262 | 1.0010 | 0.9516 | 0.0351 |
25 | 0.0146 | 1.0040 | 0.9575 | 0.0335 |
26 | 0.0209 | 0.9999 | 0.9607 | 0.0327 |
27 | 0.0178 | 0.9996 | 0.9674 | 0.0303 |
28 | 0.0173 | 0.9988 | 0.9717 | 0.0287 |
29 | -0.0434 | 1.0215 | 0.9744 | 0.0282 |
[1] |
薄景山, 李秀领, 李山有. 2003. 场地条件对地震动影响研究的若干进展[J]. 世界地震工程, 19(2): 11—15.
|
|
|
[2] |
陈国兴, 丁杰发, 方怡, 等. 2020. 场地类别分类方案研究[J]. 岩土力学, 41(11): 3509—3522.
|
|
|
[3] |
翠川三郎, 野木淑裕. 2015. 深さ30mまでの地盤の平均S波速度を深さの浅いデータから推定する方法について[J]. 日本地震工学会論文集, 15(2): 91—96.
|
|
|
[4] |
党鹏飞, 刘启方. 2019. 新疆乌鲁木齐地区VS30 经验估计研究[J]. 地震工程学报, 41(5): 1324—1331.
|
|
|
[5] |
刁守中. 2009. 20世纪山东10大地震[M]. 北京: 地震出版社.
|
|
|
[6] |
葛孚刚, 王冬雷, 许洪泰, 等. 2020. 山东地区等效剪切波速与30m等效剪切波速转换研究[J]. 科学技术与工程, 20(24): 9751—9756.
|
|
|
[7] |
贺为民, 刘明军, 杨杰. 2016. 土层剪切波速与埋深关系统计分析和应用[J]. 地震地质, 38(4): 937—949. doi: 10.3969/j.issn.0253-4967.2016.04.011.
|
|
|
[8] |
侯兴民, 薄景山, 杨学山, 等. 2004. 互相关函数在单孔法剪切波速测量中的应用[J]. 地震工程与工程振动, 24(2): 59—63.
|
|
|
[9] |
黄雅虹, 吕悦军, 兰景岩, 等. 2010. 工程场地分类中等效剪切波速计算深度问题的讨论[J]. 地震地质, 32(2): 312—319. doi: 10.3969/j.issn.0253-4967.2010.02.014.
|
|
|
[10] |
贾琳, 谢俊举, 李小军, 等. 2021. 四川和云南地区场地平均剪切波速VS20 和VS30 经验预测模型研究[J]. 地震学报, 43(5): 628—642, 679.
|
|
|
[11] |
蒋其峰, 魏玮, 王红卫, 等. 2017. 山东省场地类别分布及地震动峰值加速度区划调整[J]. 震灾防御技术, 12(3): 501—510.
|
|
|
[12] |
江志杰, 彭艳菊, 方怡, 等. 2018. 北京平原地区VS30 估算模型适用性研究[J]. 震灾防御技术, 13(1): 75—86.
|
|
|
[13] |
李洪奎, 于学峰, 耿科, 等. 2012. 山东省大地构造相研究[M]. 北京: 地质出版社.
|
|
|
[14] |
李敏, 杨立国, 陈海鹏, 等. 2020. 杭州市典型土层剪切波速与埋深间的关系分析[J]. 震灾防御技术, 15(1): 77—88.
|
|
|
[15] |
李小军. 2013. 地震动参数区划图场地条件影响调整[J]. 岩土工程学报, 35(S2): 21—29.
|
|
|
[16] |
鹿子林, 付海清, 胡超, 等. 2014. 钻孔剪切波速测试2种方法的对比[J]. 华北地震科学, 32(2): 45—49.
|
|
|
[17] |
吕悦军, 彭艳菊, 兰景岩, 等. 2008. 场地条件对地震动参数影响的关键问题[J]. 震灾防御技术, 3(2): 126—135.
|
|
|
[18] |
米欣雪, 张雨婷, 任叶飞, 等. 2023. 一种场地VS30 经验估计最佳模型的选用方法: 以新疆地区为例[J]. 西安建筑科技大学学报(自然科学版), 55(2): 288—292.
|
|
|
[19] |
彭艳菊, 吕悦军, 黄雅虹, 等. 2009. 工程地震中的场地分类方法及适用性评述[J]. 地震地质, 31(2): 349—362. doi: 10.3969/j.issn.0253-4967.2009.02.016.
|
|
|
[20] |
宋明春. 2008. 山东省大地构造格局和地质构造演化[D]. 北京: 中国地质科学院.
|
|
|
[21] |
王大任, 任叶飞, 张雨婷, 等. 2023. 一种建筑工程场地参数VS30 的外推模型修正方法[J]. 哈尔滨工业大学学报, 55(9): 1—6.
|
|
|
[22] |
王红卫, 冯志军, 刘希强, 等. 2015. 山东地区地震动峰值加速度场地效应的定量分析[J]. 地震地质, 37(1): 44—52. doi: 10.3969/j.issn.0253-4967.2015. 01.004.
|
|
|
[23] |
王志才, 贾荣光, 孙昭民, 等, 2005. 沂沭断裂带安丘-莒县断裂安丘—朱里段几何结构与活动特征[J]. 地震地质, 27(2): 212—220.
|
|
|
[24] |
王志才, 王冬雷, 许洪泰, 等. 2015. 安丘-莒县断裂北段几何结构与最新活动特征[J]. 地震地质, 37(1): 176—191. doi: 10.3969/j.issn.0253-4967.2015.01.014.
|
|
|
[25] |
喻畑, 李小军. 2015. 四川、 甘肃地区VS30 经验估计研究[J]. 地震工程学报, 37(2): 525—533.
|
|
|
[26] |
中华人民共和国住房和城乡建设部, 中华人民共和国国家质量监督检验检疫总局. 2016. 建筑抗震设计规范(GB50011- 2010)[S]. 北京: 中国建筑工业出版社: 18—20.
|
Ministry of Housing and Urban-rural Development of the People's Republic of China,General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China. 2016. Code for Seismic Design of Buildings(GB50011-2010)[S]. China Architecture & Building Press, Beijing:18—20. (in Chinese)
|
|
[27] |
周健, 李小军, 李亚琦, 等. 2021. 中美建筑抗震设计规范中工程场地类别的对比和换算关系[J]. 地震学报, 43(4): 521—532, 534.
|
|
|
[28] |
|
[29] |
|
[30] |
Building Seismic Safety Council(BSSC).2020. NEHRP recommended seismic provisions for new buildings and other structures[S]. Washington DC.
|
[31] |
|
[32] |
European Committee for Standardization. 2014. EUROCODE 8: Design of structures for earthquake resistance Part 1: General rules, seismic actions and rules for buildings [S]. London.
|
[33] |
|
[34] |
|
[35] |
|
[1] | HUANG Ya-hong, L? Yue-jun, LAN Jing-yan, SHI Chun-hua, SHI Bing-xin. DISCUSSION ON THE ISSUE OF PROPER DEPTH IN CALCULATING EQUIVALENT SHEAR WAVE VELOCITY FOR SITE CLASSIFICATION [J]. SEISMOLOGY AND GEOLOGY, 2010, 32(2): 312-319. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||