{"id":40,"date":"2025-05-20T07:59:23","date_gmt":"2025-05-19T23:59:23","guid":{"rendered":"http:\/\/www.structurevspoem.com\/?p=40"},"modified":"2025-05-20T08:00:19","modified_gmt":"2025-05-20T00:00:19","slug":"40","status":"publish","type":"post","link":"http:\/\/www.structurevspoem.com\/?p=40","title":{"rendered":"\u7ed3\u6784\u5206\u6790\u8bbe\u8ba1\u6709\u54ea\u4e9b\u5de5\u4f5c\u53ef\u4ee5\u8ba9Python\u5e72\uff1fWhat tasks in structural analysis and design can Python automate?"},"content":{"rendered":"

\u8c22\u8c22\u5e7f\u5927\u4eb2\u4eec\u7684\u5173\u6ce8\u53ca\u8f6c\u53d1\u5206\u4eab\uff0c\u79bb\u7c89\u4e1d\u7a81\u783410000\u4eba\u8fd8\u5dee9518\u4eba\uff0c\u54c8\u3002\u7531\u4e8e\u6ca1\u6709\u5230\u5404\u5927\u884c\u4e1a\u7fa4\u5230\u5904\u5206\u4eab\u6240\u4ee5\u8fd8\u662f\u86ee\u611f\u6fc0\u5173\u6ce8\u672c\u53f7\u7684\u670b\u53cb\u548c\u540c\u4e8b\u4eec\u3002\u4e3b\u8981\u662f\u89c9\u5f97\u77e5\u8bc6\u5c31\u5728\u8fd9\u91cc\uff0c\u597d\u4e1c\u897f\u5927\u5bb6\u81ea\u7136\u4f1a\u53bb\u5206\u4eab\uff0c\u6ca1\u5fc5\u8981\u6253\u6270\u5230\u5bf9\u6b64\u4e0d\u611f\u5174\u8da3\u7684\u670b\u53cb\uff0c\u540c\u65f6\u6211\u4e5f\u6ca1\u60f3\u8ba9\u5199\u6587\u6210\u4e3a\u8d1f\u62c5\u4e4b\u4e00\u3002\u4f46\u5b9e\u8bdd\u5b9e\u8bf4\uff0c\u7c89\u4e1d\u591a\u4e86\uff0c\u5199\u7684\u6b32\u671b\u4fbf\u4f1a\u5f3a\u70c8\u4e00\u4e9b\u3002\u4e0a\u4e00\u671f\u6211\u4ecb\u7ecd\u4e86\u4e00\u540d\u7ed3\u6784\u5de5\u7a0b\u5e08\u5b66\u4e60Python\u7684\u5fc3\u8def\u5386\u7a0b<\/a>\uff0c\u901a\u8fc7\u4e00\u4e9b\u4e2a\u4eba\u7ecf\u9a8c\u6d45\u8c08python\u7684\u5b66\u4e60\u4e4b\u8def\uff0c\u5f53\u7136\u6bcf\u4e2a\u4eba\u7684\u5b66\u4e60\u4e4b\u8def\u90fd\u8981\u81ea\u5df1\u53bb\u601d\u8003\uff0c\u66f4\u53ef\u8d35\u7684\u5e94\u8be5\u662f\u8bf4\u5173\u6ce8\u548c\u575a\u6301\u3002\u82b1\u82b1\u4e16\u754c\uff0c\u5206\u6563\u6211\u4eec\u6ce8\u610f\u529b\u7684\u4e8b\u60c5\u592a\u591a\u3002<\/p>\n

\u200b \u4e0a\u4e00\u7bc7\u53ef\u80fd\u52fe\u8d77<\/strong>\u4e86\u5f88\u591a\u670b\u53cb\u5b66\u4e60Python\u7684\u70ed\u60c5<\/strong>\uff0c\u4f46\u53ef\u80fd\u5927\u90e8\u5206\u4eba\u90fd\u4f1a\u4e00\u76f4\u5728print(“hello world\u201c)\u7b49\u7b80\u5355\u5e94\u7528\u9636\u6bb5\uff0c\u5c31\u597d\u50cf\u4e4b\u524d\u80cc\u56db\u516d\u7ea7\u5355\u8bcd\u8868\uff0c\u6bcf\u4e00\u6b21\u70ed\u8840\u4e0a\u5934\u7684\u65f6\u5019\u5c31\u4f1a\u7ffb\u770b\u4e00\u904d\uff0c\u8fd9\u662f\u4e00\u4ef6\u53cd\u8bbd\u7684\u4e8b\u60c5\uff1a\u5f53\u65f6\u552f\u4e00\u575a\u6301<\/strong>\u53bb\u8bb0\u5355\u8bcd\uff0c\u8bb0\u4f4f\u7684\u53ea\u6709\u5355\u8bcd\u672c\u7b2c\u4e00\u9875\u7684”abandon”\uff08\u653e\u5f03\uff09\u3002<\/strong>\u5f53\u7136\u4e5f\u6709\u4e00\u4e9b\u9ad8\u624b\uff0c\u5728\u5b66\u4e60Python\u4eceYJK\u6570\u636e\u5e93\u8bfb\u53d6\u8377\u8f7d\u5de5\u51b5\u4fe1\u606f<\/a>\u540e\uff0c\u4e3e\u4e00\u53cd\u4e09\u5199\u4e86\u8f6c\u6362yjk\u6a21\u578b\u81f3abaqus\u7a0b\u5e8f\u7684\u63a5\u53e3\u3002<\/p>\n

\u200b \u4e13\u6ce8\u53bb\u505a\u4e00\u4ef6\u4e8b\uff0c\u4e13\u6ce8\u6211\u4eec\u7684\u884c\u4e1a\u3002\u6b64\u5904\u8981\u66ff\u6211\u4eec\u884c\u4e1a\u8bf4\u53e5\u516c\u9053\u8bdd\uff0c\u77e5\u4e4e\u4e0a\u4e00\u7247\u5531\u8870\u6211\u4eec\u884c\u4e1a\u7684\uff0c\u5076\u5c14\u540c\u884c\u7fa4\u91cc\u4e5f\u6709\u60b2\u89c2\u7684\u58f0\u97f3\u3002\u5176\u5b9e\u6211\u4eec\u7684\u884c\u4e1a\u53ea\u662f\u6ca1\u6709\u4e4b\u524d\u90a3\u4e48\u7c97\u653e\u5f0f\u53d1\u5c55\u4e86\uff0c\u73b0\u5728\u7532\u65b9\u8981\u6c42\u66f4\u591a\uff0c\u8bbe\u8ba1\u4eba\u5458\u5de5\u4f5c\u91cf\u66f4\u5927\uff0c\u8bbe\u8ba1\u8d39\u5374\u6ca1\u6709\u6da8\uff0c\u6240\u4ee5\u76f8\u5bf9\u6765\u8bf4\u6211\u4eec\u7684\u884c\u4e1a\u662f\u6709\u6240\u840e\u7f29\u7684\uff0c\u4f46\u662f\u968f\u7740\u4e00\u7ebf\u57ce\u5e02\u65e7\u6539\u4ee5\u53ca\u540e\u7eed\u57ce\u5e02\u5316\u53d1\u5c55\u52a0\u901f\uff0c\u6211\u4eec\u8bbe\u8ba1\u884c\u4e1a\u4e5f\u8fd8\u7b97\u53ef\u4ee5\u7684\uff0c\u9760\u6280\u672f\u53ef\u4ee5\u6c42\u5c0f\u5eb7\u7684\uff0c\u5982\u679c\u8981\u60f3\u66b4\u5bcc\u90a3\u4f30\u8ba1\u8fd8\u662f\u5f97\u6c42\u4e70\u5f69\u7968\u4e2d\u4e00\u7b49\u5956\u4e86\uff0c\u800c\u6211\u4eec\u4e5f\u5e94\u8be5\u591a\u4e00\u4e9b\u79ef\u6781\u4e3b\u52a8\u548c\u6b63\u80fd\u91cf\u3002\u5176\u5b9e\u5f00\u59cb\u5b66\u4e60Python\u4e4b\u524d\uff0c\u6211\u4eec\u8fd8\u771f\u7684\u5fc5\u987b\u60f3\u4e00\u60f3\uff0c\u5728\u7ed3\u6784\u5206\u6790\u8bbe\u8ba1\u8fd9\u4e2a\u884c\u5f53\uff0cPython\u771f\u6b63\u80fd\u5e2e\u6211\u4eec\u505a\u54ea\u4e9b\u4e8b\u60c5\uff0c\u6709\u54ea\u4e9b\u5de5\u4f5c\u53ef\u4ee5\u8ba9Python\u53bb\u5e72\uff0c\u6211\u4eec\u8981\u6839\u636e\u6211\u4eec\u7684\u9700\u6c42\u53bb\u5b66\u3002<\/p>\n

\u200b \u5f53\u7136\u53ef\u80fd\u6709\u4e9b\u4eba\u8981\u63d0python\u4eba\u5de5\u667a\u80fd\u51fa\u56fe\u8fd9\u4e2a\u6982\u5ff5\uff0c\u5176\u5b9e\u8fd9\u79bb\u6211\u4eec\u7684\u65e5\u5e38\u8fd8\u6709\u4e00\u4e9b\u9065\u8fdc\u3002\u636e\u6211\u6240\u77e5\uff0c\u73b0\u6709\u7a0b\u5e8f\u76ee\u524d\u8fd8\u6ca1\u6cd5\u4ee3\u66ff\u4eba\u5de5\uff0c\u4eba\u5de5\u667a\u80fd\u8981\u83b7\u5f97\u81ea\u4e3b\u610f\u8bc6\u62a2\u593a\u6211\u4eec\u7684\u5de5\u4f5c\u5c97\u4f4d\u53ef\u80fd\u8fd8\u53ea\u5b58\u5728\u79d1\u5e7b\u7247\u91cc\u4e86\u3002\u90a3\u6211\u5c31\u7b80\u5355\u8bf4\u8bf4Python\u80fd\u505a\u54ea\u4e9b\u5177\u4f53\u7684\u5de5\u4f5c\u5427\uff0c\u6b22\u8fce\u5927\u5bb6\u7559\u8a00\u8865\u5145\u3002<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u200b<\/th>\n\u5e8f\u53f7 <\/font><\/th>\n\u7ed3\u6784\u8bbe\u8ba1\u6709\u54ea\u4e9b\u4e8b\u60c5\u53ef\u4ee5\u8ba9python\u505a\uff1f<\/th>\n<\/tr>\n<\/thead>\n
1 <\/font><\/td>\n\u91c7\u7528python\u53ef\u4ee5\u5b9e\u73b0ABAQUS\u53c2\u6570\u5316\u5efa\u6a21\u3002ABAQUS\u652f\u6301Python\u811a\u672c\u8bed\u8a00\uff0c\u4e5f\u53ef\u4ee5\u7f16\u5199\u540e\u5904\u7406\u53ca\u5f00\u53d1abaqus gui\u754c\u9762\uff1b<\/td>\n<\/tr>\n
————————–<\/td>\n————————————————————<\/td>\n<\/tr>\n
2 <\/font><\/td>\n\u7528python\u7f16\u5199Grasshopper\u5efa\u6a21\u53ca\u7b97\u6cd5\u7684\u7535\u6c60\u3002Rhino+Grasshopper\u7684\u652f\u6301\u811a\u672c\u8bed\u8a00gpython\uff0cpython\u5728GH\u4e2d\u8fdb\u884c\u53ef\u89c6\u5316\u7f16\u7a0b\u3002\u8f6f\u4ef6\u6709\u76f8\u5173\u7684\u5e2e\u52a9\u6587\u6863\u53ef\u4ee5\u67e5\u770b\u3002<\/td>\n<\/tr>\n
3 <\/font><\/td>\npython\u91c7\u7528ctype\u8c03\u7528sap2000 \u548cETABS \u7684OAPI\u8fdb\u884c\u5efa\u6a21\u53ca\u5206\u6790\u524d\u540e\u5904\u7406\u3002\u5177\u4f53\u53ef\u4ee5\u53c2\u8003\u4e0b\u5176OPAI\u5e2e\u52a9\u6587\u6863\u4e2d\u7684\u5b9e\u4f8b\u548c\u51fd\u6570\u3002\u975e\u89e3\u6784\u516c\u4f17\u53f7\u6700\u8fd1\u6709gh\u8fde\u63a5sap2000\u7684\u89c6\u9891\u4ecb\u7ecd\u3002\u6211\u540e\u7eed\u4e5f\u4f1a\u6709\u4e00\u4e9b\u76f8\u5173\u7684\u5f00\u53d1\u548c\u5206\u4eab\u3002<\/td>\n<\/tr>\n
4 <\/font><\/td>\npython\u7b2c\u4e09\u65b9\u5305openssepy\uff0c\u5728python\u4e2d\u53ef\u4ee5\u4f7f\u7528opensees\uff0c\u5e76\u4e14\u652f\u6301Matplotlib\u7ed8\u56fe\u3002\u4e3a\u5f00\u6e90\u7684opensees\u5728python\u4e2d\u7684\u62d3\u5c55\u3002\u7f51\u7ad9\u94fe\u63a5\uff1ahttps:\/\/openseespydoc.readthedocs.io\/en\/latest\/<\/a><\/td>\n<\/tr>\n
5 <\/font><\/td>\npython\u8bfb\u53d6\u622a\u9762\u6c42\u89e3\u5668XTRACT\u4e8c\u8fdb\u5236\u6587\u4ef6\uff0c\u5e76\u8f93\u51fa\u5230text\u4e2d\u3002\u5df2\u5c06\u6b64\u7a0b\u5e8f\u5d4c\u5165\u6211\u53f8\u8d85\u9650\u5206\u6790\u5e73\u53f0\uff0c\u5927\u5bb6\u53ef\u4ee5\u89c2\u770b\u89c6\u9891\u53f7structurevspoem\uff08\u4e0e\u516c\u4f17\u53f7\u540c\u540d\uff09\uff0c\u6709\u6b64\u8f6f\u4ef6\u5b8c\u5168\u4e0d\u7528\u4eba\u5de5\u6570\u636e\u5904\u7406\u53ca\u7ed8\u56fe\u3002<\/td>\n<\/tr>\n
6 <\/font><\/td>\npython\u6c42\u89e3\u5730\u9707\u6ce2\u53cd\u5e94\u8c31\u66f2\u7ebf\uff0c\u751f\u6210\u4eba\u5de5\u6ce2\u66f2\u7ebf\uff0c\u4fee\u6539\u6ce2\u66f2\u7ebf\u3002\u7ec8\u4e8e\u53ef\u4ee5\u7529\u5f00matlab\u590d\u6742\u7684\u5b89\u88c5\u5305\u4e86\uff0c\u653e\u5fc3\u7528python\u5427\u3002\u6700\u8fd1\u7528c++\u5199\u4e86\u4e00\u4e2a\u8bfb\u53d6\u5730\u9707\u6ce2\u751f\u6210\u8c31\u5e76\u7ed8\u56fe\u7684\u5de5\u5177\uff0c\u7a0d\u540e\u6587\u7ae0\u518d\u5206\u4eab\u5b9e\u7528\u5de5\u5177\u3002\u53ef\u4ee5\u67e5\u770b\u4e0b\u5d14\u535a\u58eb\u7f51\u7ad9\uff0c\u5434\u535a\u58eb\u53ca\u975e\u89e3\u6784\u7b49\u516c\u4f17\u53f7\u4e0a\u4e5f\u6709\u76f8\u5173\u53cd\u5e94\u8c31\u7684\u4ecb\u7ecd\u3002<\/td>\n<\/tr>\n
7 <\/font><\/td>\npython\u91c7\u7528sqlite3\u6570\u636e\u5e93\uff0c\u8bfb\u53d6yjk\u548cpkpm\u6a21\u578b\u6570\u636e\u5e93\u53ca\u8fdb\u884c\u6a21\u578b\u8f6c\u6362\u3002\u76ee\u524d\u5df2\u5b9e\u73b0yjk\u8f6copensees tcl\u7684\u51e0\u4f55\u6a21\u578b\u8f6c\u6362\u53ca\u663e\u793a\u3002\u53ef\u4ee5\u53c2\u8003\u4e0b\u535a\u6587<\/td>\n<\/tr>\n
8 <\/font><\/td>\npython\u8bfb\u53d6yjk\u6587\u672c\u7ed3\u679c\u6216ETABS\u7684mdb\u6570\u636e\u5e93\uff0c\u5e76\u5b58\u5165excel\u4e2d\u3002\u8fd9\u4e2a\u5e94\u8be5\u6700\u7b80\u5355\u4e86\uff0c\u53ea\u9700\u8981\u77e5\u9053\u6b63\u5219\u8868\u8fbe\u5f0f\u53capython\u5b57\u7b26\u4e32\u5904\u7406\u4ee5\u53caxlsxwriter\u5305\u7684\u51fd\u6570\u5c31\u53ef\u4ee5\u4e86\u3002\u6211\u524d\u671f\u5199\u7684\u4e00\u4e9b\u5c0f\u9707\u4e2d\u9707\u6570\u636e\u5904\u7406\u7a0b\u5e8f\u5c31\u662f\u8fd9\u4e2a\u5e94\u7528\uff0c\u4f46\u73b0\u5728\u6539\u6210C++\u6765\u5199\u4e86\uff0c\u6548\u7387\u4f1a\u66f4\u9ad8\u4e00\u4e9b\uff0c\u4f46python\u786e\u5b9e\u662f\u5728\u6211\u6ca1\u638c\u63e1C++\u4e4b\u524d\u6700\u597d\u7684\u5904\u7406\u5de5\u5177\u4e86\u3002<\/td>\n<\/tr>\n
9 <\/font><\/td>\npython\u5199\u4e00\u4e2a\u751f\u6210xtract\u6a21\u578b.xpj\u7684\u7a0b\u5e8f\uff0c\u5176\u5b9extract\u6a21\u578b\u4e3a\u6587\u672c\u6587\u4ef6\uff0c\u6309\u7167\u56fa\u5b9a\u7684\u683c\u5f0f\u6392\u5217\u6570\u636e\uff08\u5177\u4f53\u53ef\u4ee5\u641c\u6211\u65b0\u6d6a\u535a\u5ba2\u6709\u4e00\u4e9b\u76f8\u5173\u4ecb\u7ecd\uff09\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u590d\u6742\u7684\u578b\u94a2\u6df7\u51dd\u571f\u67f1\u622a\u9762\u3002<\/td>\n<\/tr>\n
10 <\/font><\/td>\npython\u8fdb\u884c\u5e38\u7528\u7684\u8ba1\u7b97\u8f6f\u4ef6\u6a21\u578b\u8f6c\u6362\uff0c\u4f8b\u5982sap2000\u7684s2k,etabs\u7684e2k\u53camidas gen\u7684mgt\uff0cabaqus\u7684\u7b49\u6587\u672c\u683c\u5f0f\uff0c\u90fd\u662f\u6309\u7167\u56fa\u5b9a\u89c4\u5f8b\u6392\u5217\uff0c\u5927\u6982\u5c31\u662f\u8282\u70b9\uff0c\u5355\u5143\uff0c\u6750\u6599\uff0c\u9644\u52a0\u8d28\u91cf\uff0c\u8282\u70b9\u8377\u8f7d\uff0c\u7ebf\u8377\u8f7d\uff0c\u9762\u8377\u8f7d\u7b49\u533a\u5757\u3002Python\u662f\u5904\u7406\u5b57\u7b26\u4e32\u6700\u597d\u7684\u8f6f\u4ef6\uff0c\u8f6c\u6362\u8fd9\u4e9b\u6587\u672c\u6587\u4ef6\u7684\u6548\u7387\u4e5f\u633a\u9ad8\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u200b \u5f53\u7136python\u8fd8\u6709\u5f88\u591a \uff0c\u9650\u4e8e\u4e3a\u7ed9\u5927\u5bb6\u7559\u66f4\u591a\u601d\u8003\u7a7a\u95f4\uff0c\u4e0d\u5168\u90e8\u5199\u51fa\uff0c\u5927\u5bb6\u53ef\u4ee5\u7559\u8a00\u5199\u4e0b\u81ea\u5df1\u7684\u5e94\u7528\u6216\u8005\u8bbe\u60f3\u4e5f\u53ef\u4ee5\uff0c\u4e0d\u6015\u5b9e\u73b0\u4e0d\u4e86\u5c31\u6015\u60f3\u4e0d\u5230\u3002\u5f53\u7136\u6709\u4eba\u8981\u8bf4\u722c\u866b\u548c\u8bcd\u4e91\u751f\u6210\uff0c\u7f16\u5199\u673a\u5668\u4eba\u8ddf\u81ea\u5df1\u804a\u5929\u5565\u7684\uff0c\u8fd9\u4e9b\u90fd\u4e0d\u5728\u672c\u884c\u4e1a\u7684\u8303\u56f4\u4e86\u3002<\/p>\n

\u200b \u5927\u5bb6\u6709\u4ec0\u4e48\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u7684\u65b9\u6cd5\u4e5f\u53ef\u4ee5\u5728\u6b64\u5206\u4eab\u6216\u7535\u90ae\u81f3idealzeng@qq.com\u3002\u5982\u679c\u4f60\u89c9\u5f97\u5bf9\u4f60\u6709\u6240\u542f\u53d1\u548c\u6fc0\u52b1\uff0c\u53ef\u4ee5\u70b9\u51fb”\u5728\u770b”\u6216\u8f6c\u53d1\u7ed9\u4f60\u719f\u6089\u7684\u670b\u53cb\u3002\u5982\u679c\u6709\u60f3\u6765\u6211\u53f8\u5de5\u4f5c\uff0c\u6216\u60f3\u4ea4\u6d41\u5b66\u4e60\u548c\u60f3\u63a2\u8ba8\u4e0b\u4eba\u751f\u7684\u670b\u53cb\uff0c\u4e5f\u53ef\u4ee5\u76f4\u63a5\u53d1\u7b80\u5386\u7ed9\u81f3\u6211\u90ae\u7bb1\u3002<\/p>\n

Thank you all for your attention and sharing! We are still 9,518 followers away from reaching 10,000. Since I haven’t actively promoted this account in various industry groups, I’m genuinely grateful to those who follow and support this account. I believe knowledge should be shared naturally, and there’s no need to disturb those uninterested. Meanwhile, I don’t want writing to become a burden. Frankly, more followers do motivate me to write more.<\/p>\n

In the previous article A Structural Engineer’s Journey Learning Python<\/a>, I shared personal experiences about learning Python. Everyone must find their own learning path through reflection. Persistence is crucial in this distracting world where countless things compete for our attention.<\/p>\n

The previous article might have sparked enthusiasm<\/strong> for learning Python among many friends, but most might still linger at the print("hello world")<\/code> stage. Like reviewing English vocabulary books before exams, we often revisit the first page containing “abandon” (the first word in dictionaries, meaning “to give up”). However, some experts have successfully developed tools like converting YJK models to Abaqus interfaces after learning Extracting Load Case Information from YJK Database Using Python<\/a>.<\/p>\n

Focus on one task and our industry. Let me defend our field: while Zhihu (Chinese Quora) often criticizes structural engineering, and pessimism occasionally emerges in professional groups, our industry isn’t declining\u2014it’s evolving. Clients demand more while design fees stagnate. However, with urban renewal in Tier-1 cities and accelerated urbanization, technical expertise can still secure a decent living. For wealth accumulation, you’d better rely on winning lotteries. Let’s maintain positivity and proactivity.<\/p>\n

Some may mention AI-driven automated drawing with Python, but this remains distant from daily practice. Current programs cannot replace human engineers, and AI achieving consciousness to replace us exists only in sci-fi. Here are practical Python applications in structural engineering:<\/p>\n

    \n
  1. ABAQUS Parametric Modeling<\/strong>: ABAQUS supports Python scripts for modeling, post-processing, and GUI development. <\/li>\n
  2. Grasshopper Algorithm Components<\/strong>: Use Python in Rhino+Grasshopper for visual programming. Refer to official documentation. <\/li>\n
  3. SAP2000\/ETABS OAPI Integration<\/strong>: Use Python’s ctypes<\/code> to call SAP2000\/ETABS APIs for pre\/post-processing. See OAPI examples. Non-destructive public account has SAP2000-GH integration videos. <\/li>\n
  4. OpenSeesPy<\/strong>: Python interpreter for OpenSees with Matplotlib visualization. Website: https:\/\/openseespydoc.readthedocs.io\/en\/latest\/ <\/a> <\/li>\n
  5. XTRACT Binary File Processing<\/strong>: Read XTRACT results into text files. Embedded in our company’s structural analysis platform. Watch videos on WeChat official account “structurevspoem”. <\/li>\n
  6. Seismic Wave Analysis<\/strong>: Generate response spectra, artificial waves, and modify waveforms. C++ tools under development for efficiency. <\/li>\n
  7. YJK\/PKPM Database Integration<\/strong>: Use Python’s sqlite3<\/code> to read YJK\/PKPM databases and convert models. YJK-to-OpenSees geometry conversion achieved. <\/li>\n
  8. Result Data Extraction<\/strong>: Parse YJK text results\/ETABS MDB into Excel using regex and xlsxwriter<\/code>. Previously used in small earthquake data processing (now migrated to C++). <\/li>\n
  9. XTRACT Model Generation<\/strong>: Create .xpj<\/code> files by formatting text data. See my Sina Blog for details on generating complex steel-concrete column sections. <\/li>\n
  10. Model Format Conversion<\/strong>: Convert S2K, E2K, MGT, Abaqus text formats (nodes, elements, materials, loads) using Python’s string processing capabilities. <\/li>\n<\/ol>\n

    Python offers endless possibilities\u2014leaving room for your creativity! Share ideas via comments or email (idealzeng@qq.com). If inspired, click “See Original” or share with friends. Interested in job opportunities or technical discussions? Send your resume to my email.<\/p>","protected":false},"excerpt":{"rendered":"

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