scholarly journals Construction tender price estimation standardization (TPES) in Malaysia

2018 ◽  
Vol 25 (3) ◽  
pp. 443-457 ◽  
Author(s):  
Salihudin Hassim ◽  
Ratnasamy Muniandy ◽  
Aidi Hizami Alias ◽  
Pedram Abdullah
Keyword(s):  
Neural Network ◽  
Coming Out ◽  
Cost Estimation ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Content Type ◽  
Estimation Process ◽  
Price Estimation ◽  
Fuzzy Neural ◽  
Artificial Neural Network Method

Purpose The pre-tender estimation process is still a hazy and inaccurate process, despite it has been practiced over decades, especially in Malaysia. The methods evolved over time largely depend on the amount of information available at the time of estimation. More often than not, the estimate produced during the pre-tender stage is far more than the tender cost of the project and sometimes, it is perilously underestimated and caused major problems to the client in the monetary planning. The purpose of this paper is to determine the most influential factors on the deviation of pre-tender cost estimation in Malaysia by conducting a survey. Design/methodology/approach Fuzzy logic, combined with artificial neural network method (fuzzy neural network) was then used to develop an estimating model to aid the pre-tender estimation process. Findings The results showed that the model is able to shift the cost estimation toward accuracy. This model can be used to improve the pre-tender estimation accuracy, enabling the client to take the necessary early measures in preparing the funding for a building project in Malaysia. Originality/value To the authors’ knowledge, this is the first study on tender price estimation standardization for a construction project in Malaysia. In addition, the authors have used factors from literature for the model, which shows the thoroughness of the developed model. Thus, the findings and the model developed in this study should be able to assist contractors in coming out with a more accurate tender price estimation.

Estimation of construction project building cost by back-propagation neural network

2019 ◽  
Vol 18 (3) ◽  
pp. 601-609
Author(s):  
Qinghua Jiang
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Estimation Accuracy ◽  
Average Error ◽  
Estimation Model ◽  
Content Type ◽  
Building Cost

Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

Key indexes choosing approach of complex equipment’s development cost based on grey incidence cluster model

2016 ◽  
Vol 6 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Naiming Xie ◽  
Chuanzhen Hu ◽  
Songming Yin
Keyword(s):  
Cost Estimation ◽  
Grey System Theory ◽  
Critical Parameters ◽  
Estimation Accuracy ◽  
Content Type ◽  
Complex Products ◽  
Civil Aircraft ◽  
Complex Equipment ◽  
Key Indicators ◽  
The Cost

Purpose – The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc. Design/methodology/approach – Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree. Findings – The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved. Practical implications – The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model. Originality/value – In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

scholarly journals An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1317
Author(s):  
Xin Huang ◽  
Yuanping Zhu ◽  
Shuqin Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Human Action ◽  
Human Motion ◽  
Estimation Accuracy ◽  
Human Motion Analysis ◽  
Critical Feature ◽  
Fuzzy Neural ◽  
Motion Retrieval

Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

Estimating a civil aircraft’s development cost with a GM(1, N) model and an MLP neural network

2017 ◽  
Vol 7 (1) ◽  
pp. 2-18 ◽  
Author(s):  
Nai-ming Xie ◽  
Song-Ming Yin ◽  
Chuan-Zhen Hu
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Development Cost ◽  
Combined Approach ◽  
Limited Data ◽  
New Approach ◽  
Content Type ◽  
Development Costs ◽  
Civil Aircraft ◽  
New Type

Purpose The purpose of this paper is to study a new approach by combining a multilayer perceptron neural network (MLPNN) algorithm with a GM(1, N) model in order to estimate the development cost of a new type of aircraft. Design/methodology/approach First, data about developing costs and their influencing factors were collected for several types of Boeing and Airbus aircraft. Second, a GM(1, N) model was constructed to simulate development costs for a civil aircraft. Then, an MLPNN algorithm was added to optimize and revise the simulative and forecasting values. Finally, a combined approach, using both a GM(1, N) model and an MLPNN algorithm was adopted to forecast development costs for new civil aircraft. Findings The results show that the proposed approach could do the work of cost estimation for new types of aircraft. Rather than using a single model, the combined approach could improve simulative and forecasting accuracy. Practical implications Scientific cost estimation could improve management efficiency and promote the success of a new type of civil aircraft development. Considering that China’s civil aircraft research and development is at its very beginning stages, only very limited data could be collected. The development costs for civil aircraft are affected by a series of factors. The approach outlined by this paper could be applied to development cost estimations in China’s civil aircraft industry. Originality/value The paper has succeeded by constructing a cost estimation index system and proposing a novel combined cost estimation approach comprised of a GM(1, N) model and an MLPNN. It has undoubtedly contributed to improving the accuracy of cost estimations.

scholarly journals Improving the cost estimates of complex projects in the project-based industries

2017 ◽  
Vol 7 (2) ◽  
pp. 173-184 ◽  
Author(s):  
Pournima Sridarran ◽  
Kaushal Keraminiyage ◽  
Leon Herszon
Keyword(s):  
Best Practices ◽  
Cost Estimation ◽  
Estimation Accuracy ◽  
Effective Practices ◽  
Accurate Estimation ◽  
Content Type ◽  
Complex Projects ◽  
Cost Implications ◽  
The Cost ◽  
Improve Cost

Purpose Project-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy. Design/methodology/approach An online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects. Findings Study results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects. Originality/value The study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.

Estimating civil aircraft’s research and manufacture cost by using grey system model and neural network algorithm

2015 ◽  
Vol 5 (1) ◽  
pp. 89-104 ◽  
Author(s):  
Naiming Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Relational Model ◽  
Content Type ◽  
Network Algorithm ◽  
Parameters Selection ◽  
Civil Aircraft ◽  
Neural Network Algorithm ◽  
Grey Relational

Purpose – The purpose of this paper is to propose novel civil aircraft cost parameters’ selection method and novel cost estimation approach for civil aircraft so as to effectively simulate or forecast civil aircraft cost under poor information and small sample. Design/methodology/approach – Based on existent cost estimation indexes, this paper summarized civil aircraft research and manufacturing cost impact index system and adopted grey relational model to select most important impact factors. Consider civil aircrafts’ cost information could not be easily collected, the author must estimate their costs with limited sample and poor information. A combination model of GM (0, N) model and BP neural network algorithm is proposed. Both advantages of simulation of BP neural network algorithm and poor information generation of GM (0, N) were effectively combined. Then steps of combined model were given out. Finally, nine types of aircrafts were used to test the validity of proposed model. As comparing with the traditional multiple linear regression model and simple GM (0, N) model, results indicated that proposed model can do the work better. Findings – Grey relational model can be applied for parameters’ selection and combined GM (0, N) model and BP neural network algorithm can estimate aircraft’s cost as well. Results show that novel combined model could get high forecasting accuracy. Practical implications – Cost estimation is key problem in production management of civil aircraft. Effective cost management could promote competitiveness of aircraft manufacturing company. Proposed combined model can be applied for civil aircraft cost estimation. Similarly, it could be applied for other complex equipment cost estimation. Originality/value – The paper succeeds in proposing grey relational model for cost parameters’ selection and constructing a combination model of GM (0, N) model and BP neural network algorithm. Algorithm of the proposed model was discussed and steps were given out.

Using photographs and metadata to estimate house prices in South Korea

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Changro Lee ◽  
Key-Ho Park
Keyword(s):  
Neural Network ◽  
South Korea ◽  
Visual Information ◽  
House Prices ◽  
House Price ◽  
Content Type ◽  
Real Estate Valuation ◽  
Valuation Process ◽  
Price Estimation ◽  
The Impact

PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

Gain adaptive sliding mode controller based on interval type-II fuzzy neural network designed for attitude control for micro aircraft vehicle

2014 ◽  
Vol 7 (2) ◽  
pp. 209-226 ◽  
Author(s):  
Xiangjian Chen ◽  
Di Li ◽  
Zhijun Xu ◽  
Yue Bai
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Type Ii ◽  
Content Type ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Fuzzy Neural ◽  
Interval Type

Purpose – Micro aerial vehicle is nonlinear plant; it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances. Design/methodology/approach – First, by employing interval type-II fuzzy neural network (ITIIFNN) to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of micro aircraft vehicle (MAV). Then, the Lyapunov stability theorem is used to testify the asymptotic stability of the closed-loop system, the parameters of the ITIIFNN and gain of sliding mode control can be tuned on-line by adaptive laws based on Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. Findings – The validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of interval type-II fuzzy neural network based gain adaptive sliding mode controller (GASMC-ITIIFNN) is significantly improved compared with conventional adaptive sliding mode controller (CASMC), type-I fuzzy neural network based sliding mode controller (GASMC-TIFNN). Practical implications – This approach has been used in one MAV, the controller works well, and which could guarantee the MAV control system with good performances under uncertainties, variations, and external disturbances. Originality/value – The main original contributions of this paper are: the proposed control scheme makes full use of the nominal model of the MAV attitude control model; the overall closed-loop control system is globally stable demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the MAV attitude control system based on GASMC-ITIIFNN controller can achieve favourable tracking performance than GASMC-TIFNN and CASMC.

scholarly journals Cost Estimation Model Using Modified Parameters for Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Sae-Hyun Ji ◽  
Joseph Ahn ◽  
Hyun-Soo Lee ◽  
Kyeongjin Han
Keyword(s):  
Cost Estimation ◽  
Construction Projects ◽  
Cost Model ◽  
Influence Factors ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Proposed Model ◽  
Cost Estimation Model ◽  
The Cost

Construction projects require huge amounts of capital and have many risk factors due to the unique industry characteristics. For a project to be successful, accurate cost estimation during the design phase is very important. Thus, this research aims to develop a cost estimation model where a modification method integrates influential factors with significant parameters. This study identified a modified parameter-making process, which integrates many influential factors into a small number of significant parameters. The proposed model estimates the cost using quantity-based modified parameters multiplied by their price. A case study was conducted with 24-residence building project, and the estimation accuracy of the suggested method and a CBR model were compared. The proposed model achieved higher overall cost-estimation accuracy and stability. A large number of influence factors can be modified as simple representatives and overcome the limitations of a conventional cost estimation model. The paper originality relates to providing a modified parameter-making process to enhance reliability of a cost estimation. In addition, the suggested cost model can actively respond to the iterative requirements of recalculation of the cost.

Sign in / Sign up

Export Citation Format

Share Document