CAJ | 학술논문

建设项目的复杂性、需要的完工时间充满着不确定性等问题，对于进度管理者而言，让建设项目在限期内完工，是一个重要课题．针对具有多条相似关键线路的工程计划网络总工期估计误差偏大，演算过程复杂，求解速度慢的问题，提出了一种基于BP神经网络的双线路项目工期估计方法．经过算例分析，基于BP神经网络的双线路项目工期估计方法，与传统的计划评审技术(PERT)相比，在工期求解速度上更为快速和便捷，在结果上具有相对精确性和可靠度，其偏差在可接受的1.5%之内，因此具有一定实用价值．
건설항목적복잡성、수요적완공시간충만착불학정성등문제，대우진도관리자이언，양건설항목재한기내완공，시일개중요과제．침대구유다조상사관건선로적공정계화망락총공기고계오차편대，연산과정복잡，구해속도만적문제，제출료일충기우BP신경망락적쌍선로항목공기고계방법．경과산례분석，기우BP신경망락적쌍선로항목공기고계방법，여전통적계화평심기술(PERT)상비，재공기구해속도상경위쾌속화편첩，재결과상구유상대정학성화가고도，기편차재가접수적1.5%지내，인차구유일정실용개치．
Construction projects are often quite complex with a lot of uncertainty about the required duration. For a project planner, it is important to ensure project completion within the deadline. In the construction environment, the activity time can be seen as a random variable, so the project duration can also be seen as a random variable. It is rather complicated to obtain the probability distribution for project duration using analytical or numerical methods, while simulation through a large number of samplings can only produce an approximate solution. Therefore, this research proposes the double paths BP neural networks evaluation technique. The neural network model can reduce the amount of calculation in estimating project duration and obtain a speedier solution than PERT. The results of applying the neural networks developed from this research to estimating the mean duration and the duration for a given confidence level for two project cases show that they can achieve a mean absolute percentage errors within 1.5%.