Improved Construction Subcontractor Evaluation Performance Using ESIM
نویسندگان
چکیده
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. & The evaluation of potential subcontractors is a complex task for construction companies. The success of such evaluations currently relies heavily on personal factors that include management experience and intuition. The object of this study was to propose a support model that would improve current subcontractor performance evaluation practices. The appropriateness of employing the Evolutionary Support Vector Machine Inference Model (ESIM) in evaluation procedures was studied and analyzed, and a Subcontractor Rating Evaluation Model (SREM) was developed by adapting the ESIM to fit subcontractor performance cases in the historical record. The effectiveness of the proposed SRPM was subsequently validated in a case study on an actual general contractor. The proposed method assigned ratings to subcontractors that were substantively the same as ratings assigned by traditional means. Results demonstrate the value of employing the proposed SREM in subcontractor evaluations.
منابع مشابه
An On-site subcontractor evaluation method based on lean principles and partnering practices
Subcontracting has greatly increased in the construction industry. It helps shift risk from the main contractor to the subcontractor and promotes specialization. Inadequate subcontracting management, however, may result in an adversarial relationship between main contractors and their subcontractors, uncoordinated on-site execution, and disappointing quality and schedule fulfillment. New subcon...
متن کاملEvaluating subcontractor performance using evolutionary fuzzy hybrid neural network
This paper developed an evolutionary fuzzy hybrid neural network (EFHNN) to enhance the effectiveness of assessing subcontractor performance in the construction industry. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and non-linear NN layer ...
متن کاملPredicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks
Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NN...
متن کاملDynamic Prediction of Project Success Using Evolutionary Support Vector Machine Inference Model
The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an Evolutionary Support Vector Machine Inference Model (ESIM). ESIM is developed based on a hybrid approach that fuses support vector machine (SVM) and fast messy genetic algorithm (fmGA). SVM ...
متن کاملAutomated Processing of Subcontractor Work Performance Data to Improve the Quality Control and Support the Subcontractor Selection Process
The paper presents the research aimed to find the way for efficient monitoring and control of subcontractor performance in large construction projects. As a result the procedure for recording and processing the nonconformities is proposed. Procedure consists of the following stages: the identification of nonconformity (defect, work performed is at variance with project, the requirements of work...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Applied Artificial Intelligence
دوره 26 شماره
صفحات -
تاریخ انتشار 2012