深基坑降水函数的构建与智能实现

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Abstract：Inordertonhancethescientifcandintellgentlevelofdeepfoundationpitdewateringdesign，amethdforconstuctinga foundationpitdewateringfunctionbycombiningumericalsmulationandmachineleamingwasproposed takingShanghaiasthersearch backgroudBasedonthetypicalgoundwater-landsubsidenedualcontrolonngoffoundationpidewateringiSanghaiaate dimensional groundwater numerical model was established for partitions and ⑦112-3 .The influence of factors，including foundation pit area，aspectatioexcavatiodepthandcurtainpenetrationdepthintoteaquiferonthegoundwaterdrawdoatdstacef 0.5H⋅ 1H，2H，and3H（ H is the excavation depth of the foundation pit）outside the pit were analysed.On this basis，multiple linear regression method wasusedtoconstruct explicitfunctionalrelationships，andBPandMLPneuralnetwork modelswereutilizedtoimprovethe acuracyandapplicabilityofdrawdownprediction.Theresearchresultsindicatethatneuralnetworkmodelshavegoodfitingability for nonlinearrelationshipsbetweencomplexvariables，andthepredictedvaluesarehighlyconsistent withthe measureddatahichcan achieveapidandintelligentpredictionof groundwaterdrawdownatvarious monitoringpointsoutsidetefoundationpit.Teresearch provides new methodsand technical support for optimizing deep foundation pit dewatering schemes in complexurban areas.

Key words： deep foundation pit;dewatering prediction; numerical simulation; BP neural network; MLP neural network为制约工程安全与城市地质环境稳定的关键因素之一[1-2]。（剩余14550字）