基于PSO-BP神經(jīng)網(wǎng)絡高速公路建設期碳排放預測方法

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中圖分類號：U415 文獻標識碼：A DOI：10.7535/hbkd.2025yx03009

Carbon emission prediction method for expressway construction period based on PSO-BP neural network

ZHAO Quansheng1，LI Fei1，GUO Feng'ai1，YU Jianyou 2 ，XU Shizhao 3 ， HU Yunpeng4，CHU Xiaomeng5

ool of Civil Engineering，Hebei Universityof Science and Technology，Shijiazhuang，Hebei O5ool8，China; 2.Hebei Expressway Han Gang Port Company Limited，Cangzhou，Hebei O6l5oo，China;

3.SchoolofMechanicalEngineering，Hebei UniversityofScienceand Technology，Shijiazhuang，HebeiO5oo18，China; 4.Qinhuangdao Highway Construction and Development Center，Qinhuangdao，Hebei O66o99，China; 5.School of Chemical and Pharmaceutical Engineering，Hebei University of Science and Technology，Shijiazhuang， Hehei .China）

Abstract：To solvetheproblemof inaccuratecarbonemissons predictionduring the highwayconstruction period，amethodof Optimizing theback propagation（BP）neuralnetwork byparticleswarmoptimization（PSO）algorithmwas proposed topredictcarbon emisions.The12keyndicators，includingroutelength，subgradelength，pavementlngth，tunellength，ridgeandculvertlength， interchangelength，excavationvolume，filingvoue，dieselconsumption，cementconsumption，crushedstoneconsumptionadteel consumption，wererefinedfromthefourdimensionsof project length，construction，energyconsumptionandmaterialconsumption usingtheanalytichierarchyprocess（AHP）.Thedatafrom36hghwayprojectswereusedasempiricalsamplesformodelrainng，nd a comparative analysis was conducted based on error indicators. The results show that the R2 value of the obtained PSO-BP model is 0.974，while the R2 value of the BP model is0.89o，with the former being closer to1.Compared to the results of life cycle assessment，thePSO-BPmodelhasasmalerdeviationfromtheactualvaluethantheunoptimizedBPmodel.Thefourlayersof the hierarchyandtheselected12keyindicatorsenablethepredictionofcarbonemisionsduringthedesignandplaningstageofhighway construction，providing reference for low-carbon highway construction.

Keywords：otherdisciplines of road engineering;carbon emisson prediction；PSO-BP neural network；modeloptimization; factor analysis

開展碳排放量預測是研究高速公路建設期碳達峰、碳中和的基礎工作，這一工作不僅是制定減排策略的前置步驟，更是評估環(huán)境影響的關鍵工具[1-2]。（剩余13746字）