基于深度自編碼器的混凝土壩變形異常檢測模型

打開文本圖片集

關(guān)鍵詞：混凝土壩；變形監(jiān)測；異常檢測；自編碼器；深度學(xué)習(xí)中圖分類號：TV642 文獻(xiàn)標(biāo)志碼：A doi：10.3969/j.issn.1000-1379.2025.07.022用格式：，，，等.基于深度自編碼器的混凝土壩變形異常檢測模型[J].人民黃河，2025，47（7）：137-143.

Concrete Dam Deformation Anomaly Detection Model Based on Deep Autoencoder

KANG Xinyu1，LI Yanlong'，ZHANGYe1，ZHOU Tao2，ZHONGWen’，YANG Tao3 （1.StateKeyLaboratoryofWaterEgineingEcologndEvoeninAridAra，XianUivesityofTcholog，Xianina; 2.Huanghe Hydropower DevelopmentCo.，Ltd.，Xining 810o0，China；3.ChinaYangtzePowerCo.，Ltd.，Yibin 644612，China） Abstract：Anunsupervisedanomalydetectionmodelbasedonadputoencoderwasproposedtoaddressanomalousreadingsinconcrete damdeformationmonitoring，withtheobjectiveofenhancingdetectionacuracyandautomation.Theautoencoderwas trainedinanusuper visedmaneronnoraldefomationdatatolearlow-dimensionalfeaturerepresentationsandwassubsequentlyemploedtorebuildcoming measurements.Measurementsexhitingsignificantdeviationsetwenobservedandrebuildvalueswereclasifiedasanomales.Teresult showsthat theproposedmodelachievesover97%accuracyianomalydetectionandisdemonstratedtoperforeliablyundervarioutesting conditions.Conseqentlytheeutocoderbdroachisableofectielydentigfoatiaolsioeda， exhibiting robust and precise detection capabilities.

Key words： concrete dam； deformation monitoring;anomaly detection；autoencoder; deep learning

0 引言

混凝土項具有卓越的承載能力、優(yōu)異的耐久性及良好的適應(yīng)性，已成為水庫大壩建設(shè)的首選類型[1-4]然而，在混凝土壩長期服役過程中，外部荷載變化（如水位波動、氣溫變化等）與內(nèi)部物理化學(xué)侵蝕（如碳化、硫酸鹽侵蝕等）持續(xù)對壩體造成負(fù)面影響，威脅大壩的安全運行[5]。（剩余10445字）