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A WICE approach to real-time construction cost estimation
Automation in Construction 15 (2006) 12 – 19 www.elsevier.com/locate/autcon
A WICE approach to real-time construction cost estimation Wen-der Yu a,*, Chien-chung Lai b, Wan-li Lee b a
Department of Construction Engineering, CHU, Hsinchu, Taiwan Information Network Department (iNet), China Engineering Consultants, Inc., Taipei, Taiwan
b
Accepted 31 January 2005
Abstract Real-time response to construction cost estimation request is crucial for construction firms to survive and grow in the industry. However, no existing construction cost estimating system fulfills this need thus far. This paper describes a joint effort, named Web-based Intelligent Cost Estimator (WICE), by the academia and the industrial partner on developing such a system. Advanced web-based intelligence techniques employed in the proposed system include WWW, neuro-fuzzy system, and data mining. The industrial partner is in charge of providing knowledge sources, including expert judgments and historical data, for conceptual cost estimation. The proposed WICE is the firstof-a-kind real-time conceptual cost estimating system in practice use. The testing results show that the proposed system provides not only a globally accessible and promptly responding means for cost estimation, but also an effective and reliable tool for real-time decision-making. D 2005 Elsevier B.V. All rights reserved. Keywords: Conceptual cost estimation; Neuro-fuzzy system; Intelligent web agent
1. Introduction Successful management of construction cost within the project budget plays a key role among the three goals of construction project management (i.e., cost within budget, schedule on time, and quality as specified). In order to achieve this major objective, accurate and reliable cost estimations should be maintained throughout the project lifecycle [1]. There have been three categories of estimations widely utilized in the area of construction management depending on the available information and required accuracy: (1) order of magnitude estimation—which is obtained with hypothetical design information and minimum site information so as to achieve the lowest accuracy; (2) conceptual estimation—with primitive design and construction site information, and to achieve low to high level of accuracy; and (3) detail estimation—with complete project design, specification, and environmental information, to achieve the highest level of accuracy. Since the third
* Corresponding author. Tel.: +886 3 5186748; fax: +886 3 5370517. E-mail address: [email protected] (W. Yu). 0926-5805/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2005.01.005
category, the detail estimation, can be achieved only when the detail designs are completed, it is not feasible in the early stages of a project, such as the conceptualizing and planning stages. As a result, the conceptual estimating methods are often adopted instead. The conceptual cost estimation during engineering planning is the most important for a construction project, since the main structural systems, major construction methods, and most construction materials are determined in that stage. However, due to the lack of detail design information during the planning phase, the accurate cost estimation becomes a difficult task for professional estimators. It was found that the estimators with more estimating experience can do better in this job than who do with less. With the emerging development of modern artificial intelligence (AI) techniques, such as neuro-fuzzy systems, the aforementioned estimating experience (knowledge) can be acquired by learning from historical examples, so that accurate estimation (compared with the detail estimation) could be obtained with very limited available project information. Unfortunately, an essential difficulty is facing the traditional conceptual cost estimation if the knowledgebased approaches are to be adopted—the unit prices of the
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
cost items are variable in the marketplace, so that the estimation knowledge learned previously may not be readily applicable in the future projects. In this paper, a novel method named ‘‘principal items ratio estimation method (PIREM) [16]’’ is employed to reflect the current unit prices of cost items. According to previous experiences, the applications of knowledge-based systems in the fields of construction engineering and management confront a major problem— the update of knowledge base and maintenance of software systems [2]. It is conceived that providing the easiest accessing means is the best way to avoid misuse of out-ofdate information. In this paper, a web-based cost estimation system is developed, which combines a neuro-fuzzy soft computing technique and the proposed PIREM conceptual cost estimating method to form an intelligent web agent (named ‘‘Web-based Intelligent Cost Estimator, WICE’’) for real-time construction cost estimations. The paper is presented in the following manner: the proposed PIREM conceptual cost estimating method is described in detail in Section 2. The application of a neuro-fuzzy soft computing technique (ANFIS [3]) for mining of conceptual cost estimation knowledge from historical data is presented in Section 3. The proposed WICE system is implemented in Section 4. System verification and testing results are shown in Section 5. In Section 6, the benefits of the proposed WICE are analyzed and discussed. Finally, findings of the research are concluded and directions for future research are recommended.
2. Principal items ratio estimation method (PIREM) Among the many conceptual estimating methods, parametric cost estimating has been widely applied in the industry for economic feasibility analysis in the early stage of a construction project. The parametric cost estimating takes important influential parameters as inputs, such as the floor area, cubic volume, bay width, etc. By statistic regression or other mapping schemes, the relations between the estimated costs and the influential parameters are established. The cost estimates of new projects are obtained by mapping inputs of parameter values based on the pre-determined mathematical relation [4]. 2.1. Problems with traditional conceptual estimating methods
13
causing this problem is that all existing analog-based estimation approaches adopt ‘‘activity cost’’ as the measure of estimates during analogizing process. This has mixed up the two elements of a construction cost item—(1) quantity of the cost item, and (2) unit price of the cost item. Even though a previous approach introduced the ‘‘overall price index’’ as a parameter for construction cost adjustments [5], the price fluctuations for individual cost items do not vary simultaneously in the same way. Adjusting the unit prices of all cost items with a single ‘‘overall price index’’ does not reflect the reality of unit price variation in the market place. A better way is to divide the two elements of the cost item and handle them separately. The quantity of a cost item could be affected by several attributes, such as the dimensions of structural design, the method of construction, the conditions of site environment, etc. The value of this element will not change as long as the same structure is to be constructed by the same method under the same environmental conditions. On the other hand, the unit price of the cost item may vary from time to time. It is reasonable to utilize the most updated unit prices for the cost items in order to reflect the real situation of the current marketplace. Another problem is that a construction project usually consists of hundreds or even thousands of cost items. It is very expensive and time consuming (if not impossible) to obtain the quantity estimates and unit prices of all items in a construction project. The Pareto Optimum Criterion (or named ‘‘80/20 Principle’’) provides a compromise for the above problem. Simply stated, the Pareto Optimum Criterion suggests that 80% of the overall project cost is determined by 20% of the cost items [6]. Therefore, instead of estimating the quantities of all cost items, only the top 20% most important cost items’ quantities are estimated, and their related unit prices are inquired. 2.2. Model of PIREM With the Pareto Optimum Criterion, almost 80% of estimation cost and time can be saved. Thus, it does not only reduce the cost but also expedite the process of estimation, and more importantly provide a feasible solution for real-time cost estimation. The selected top 20% cost items is called ‘‘Principal Items (PI)’’. The summation of quantities and unit prices of the principal cost items constitutes the Cost of Principal Items (PIC). The ratio of PIC over the overall cost is defined as the Principal Item Ratio (PIR or p). The value of p can be calculated by the following equation: l
As described in the previous section, the essential problem for all analog-based estimation approaches (such as those mentioned above) is that the unit prices of cost items are fluctuating along time. Thus, construction cost estimates obtained based on previous estimation experiences may fail due to unit price variation. The reason
ps ¼
~ UPsj Qsj j¼1 n
~ UPsl Qsi
¼
PICs ; OCs
ð1Þ
i¼1
where the numerator is the summation of the costs of all principal items, while the denominator is the summation of
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
the costs of all cost items in a project. The superscript s upon all parameters stands for the sth historical example. UPi means unit price and Q i means quantity of the ith cost item, and so forth. OC stands for overall cost. The ratio p obtained in Eq. (1) is called Principal Item Ratio (PIR) as defined above. It is found by analyzing PIRs obtained from historical cost estimation data that the PIR of a specific type of construction project usually keeps constant with very small variation [7]. Therefore, given the PIC, the OC of the new project can be recovered by the following equation: OCr ¼
PICr ; p¯
ð2Þ
where OCr is the estimate of overall cost for the new (rth) project, p¯ is the average PIR calculated from previous projects, and PICr is the cost of principal items of the new (rth) project. In Eqs. (1) and (2), the current unit prices (UPs) for the new project should be inquired from the marketplace at the moment of estimation, while the quantity, Q, and average PIR, p¯ , have to be determined by parametric estimating methods based on the pre-determined mathematical relationships. According to the literature, the mathematical relations between input parameters and the output estimate values are usually nonlinear [4,8]. In order to establish such relations, some approaches have been proposed including: (1) nonlinear regression [8], (2) artificial neural networks [9], and (3) case-based reasoning [5]. In the above, the nonlinear regression method may encounter severe convergence difficulty while dealing with more than two variables. The artificial neural network approach is good at nonlinear mapping. However, the mathematical relationships established by artificial neural networks are stored among the connection weights of the network, which is regarded as a ‘‘black box’’ to human decision makers and thus limits the value of knowledge learned [10]. The estimation error of case-based reasoning method is still as high as 15 –20% [5], which may not be accurate enough for decision-making during conceptual planning stage. In the proposed system, a neuro-fuzzy soft computing technique, ANFIS [3], is adopted to achieve more accurate estimations and provide human understandable knowledge for the estimators.
3. Mining of historical estimation data with neuro-fuzzy soft computing techniques This section describes the application of the neuro-fuzzy soft computing techniques for acquiring estimation knowledge from previous projects, so that the nonlinear relationships between the influential attributes and cost estimates can be established. The technology for extracting useful information from historical data for decision makers is named data mining (DM) [1,11]. Here, the generic process of data mining
is described and followed by the application of a neuro-fuzzy approach for data mining. 3.1. Process of data mining Data mining was defined as the application of automated knowledge acquisition methods for generation of useful knowledge via organization and analysis of raw data [11]. The procedure for data mining implementation consists of five steps [12]: (1) objective determination; (2) data preparation; (3) data transformation; (4) data mining; and (5) result analysis. Two key issues for data mining are [1]: (1) the accuracy of knowledge acquisition, and (2) the format knowledge representation. In this paper, a neurofuzzy system named ANFIS is adopted for the purpose of data mining for cost estimation primarily due to the excellent learning ability and explicit knowledge representation of neuro-fuzzy systems. 3.2. Neuro-fuzzy soft computing techniques Neuro-fuzzy system is a branch of artificial intelligence (AI) techniques, which combines the merits of artificial neural networks (ANN) and fuzzy inference systems (FIS). A basic structure of an FIS is comprised of three components [13]: (1) a rule base, which stores a bunch of fuzzy if – then rules; (2) a database, which defines the membership functions used in the fuzzy rules; and (3) a reasoning mechanism, which performs fuzzy inference upon the rules and given facts to derive a reasoning output. 3.2.1. Fuzzy inference systems (FIS) There are three major types of FIS [13]: Mamdani, Sugeno, and Tsukamoto. Following briefly is a review of the three FIS. Mamdani FIS is a general type of FIS that adopts ‘‘max’’ and ‘‘algebraic product’’ for fuzzy T-norm and T-conorm operations. A typical fuzzy if – then rule for Mamdani FIS is shown in Eq. (3): Rk : If xl is Ak1 and . . . and xp is Akp ; then y is Bk ;
ð3Þ
where R k means the kth fuzzy rule; Aki and B k represent T fuzzy linguistic variables; x¯ ¼ x1 ; x2 ; . . . ; xp ˛Rp and y˛R are the inputs and output of the kth fuzzy rule. Sugeno FIS is a special type of FIS that adopts a crisp function in the consequence of a fuzzy decision rule. A fuzzy if – then rule for Sugeno FIS is shown in Eq. (4): Rk : If x1 is Akl and . . . and xp is Akp ; then y is fk ðx1 ;x2 ;. . .;xp Þ;
ð4Þ
where R k , Aki , B k , x¯ = (x 1,x 2,. . .,x p )T and y are defined similarly as in Eq. (1), while f k (x 1,x 2,. . .,x p ) is a polynomial taking on x 1,x 2,. . .,x p and is used to define the consequence of a fuzzy decision rule.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
Tsukamoto FIS is also a special type of FIS that adopts a monotonical membership function in the consequent part of a fuzzy decision rule. A typical fuzzy decision rule for Tsukamoto FIS is shown in Eq. (5): Rk : If x1 is Ak1 andN and xp is Akp ; then y is Ck :
ð5Þ
where R k , Aki , B k , (x 1,x 2,. . .,x p )T, and y are defined similarly as in Eq. (1), while C k are monotonical membership functions used to describe the consequent part of a fuzzy decision rule. 3.2.2. Learning schemes for FIS While applying to data mining, the three FIS mentioned above need to be equipped with ‘‘learning abilities’’ so that they are able to ‘‘mine’’ knowledge from raw data. Several schemes can be used for constructing a neuro-fuzzy system such as: (1) FALCON proposed by Lin and Lee [14], (2) ANFIS proposed by Jang [3], and (3) back-propagation fuzzy system proposed by Wang and Mendel [15]. In this paper, the ANFIS scheme is adopted for constructing a Sugeno FIS. For details of ANFIS neuro-fuzzy learning scheme, the readers are referred to excellent text by Jang et al. [13].
4. Implementation of Web-based Intelligent Cost Estimator (WICE) 4.1. Conceptual planning of WICE While the integration of PIREM cost estimation approach and ANFIS neuro-fuzzy system provides the functions of an intelligent cost estimating system, the update of the knowledge base and the maintenance of the software system are tedious routines and may cause significant cost burden for the system developer. The best policy should minimize the aforementioned routines and centralize the knowledge base management tasks. An intuitive solution is to implement the proposed system on the web. The conceptual planning of the proposed WICE system is shown in Fig. 1.
WICE Users around the world Parameters inputs
Project A
15
Three critical concerns for planning WICE are: (1) lowest maintenance costs—the system should adopt centralized data and knowledge base management; (2) global and all-time accessibility—the system should adopt webbased application with Internet connection; and (3) real-time response—the system should minimize the online calculation requirements. To meet all three requirements described above, the proposed WICE system is planned as an Internet-based application program that resides on the server of the Internet Service Provider (ISP), so that the system is maintained solely by the ISP and the user around the world can access WICE via the World Wide Web without any time or location restrictions. To reduce the online calculation time, the ANFIS network mapping is readily suitable for this end. 4.2. Framework and data processing of WICE The Matlabi 6.3 with Web Server and Fuzzy Logic Toolboxes is adopted for WICE development. The platform selected for WICE implementation consists of: (1) PC CPU: Pentium IV 1.5G; (2) OS: Windows 2000 Server; and (3) SRAM: 1.0 G. Since the ANFIS trainings can be done offline, the CPU speed for the web server may be not demanding. However, broader bandwidth for transmission is required if the number of simultaneous accesses or need for graphic transmissions is large. The framework and data flows of WICE are depicted in Fig. 2. As depicted in Fig. 2, Client (User) A submits parameter inputs (including influential attributes and unit prices) and job requests to WICE by filling out the Parameter Input Sheet (see Fig. 4). The WICE then transmits the input data to Matlab main system via the interface and web server programs. The Matlab main system consults related Matlab programs and performs the required calculations (including ANFIS network mapping), and then the results (including estimation results and relevant graphics) are generated. The numeric data (resulted estimates) and graphics are handled separately hereafter. The numeric data are transmitted backward via web server and interface programs to the responding form (Estimation Report Sheet; see Fig. 5) of the client. Meanwhile, the generated graphics (if applicable) are loaded on the responding form (such as Sensitivity Analysis Report Sheet; see Fig. 6). Thus the request of Client A is completely served. Multiple requests by different users are served simultaneously via the similar process for Client A described above, while the buffer of memory (i.e., capacity of RAM) for storing intermediate data is required as the number of simultaneous users increases. The intermediate data are handled by the web server and interface programs so that no conflict occurs while multiple users are accessing WICE. 4.3. System deployment
Project B
Project C
Fig. 1. Conceptual planning of the proposed system.
The WICE system is deployed to meet the practical requirements of the industrial partner. In total, eight main
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
HTML respond form
HTML request form
W I C E
5. Verification and testing
Client B
Client A
HTML request form
HTML respond form
In order to ensure the performance of the proposed system, verification and testing are performed for PIREM and WICE, respectively. 5.1. Verification of PIREM cost estimation method
HTTP Daemon
The proposed PIREM is founded on several predeveloped principles or methods: (1) Pareto Optimum Criterion (80/20 principle); (2) ratio estimation; and (3) nonlinear mapping of ANFIS. All these principles and methods should be verified under the given conditions that are confronted in construction cost estimation to show their applicability.
Interface program Generated graphics
Web Server Matlab Main System
Data warehouse
Fuzzy Logic Toolbox
Knowledge base
Fig. 2. Framework and data flows of WICE.
modules are developed according to the type of construction projects, while each module consists of one to five sub-modules to deal with special works within each project type. The eight main project types are: (1) reinforced concrete bridges; (2) drainage works; (3) Earth moving; (4) dike construction; (5) retaining walls; (6) slope protection works; (7) pavement works; and (8) building construction. In total, 21 sub-modules are developed in WICE. The entry page of WICE system is shown in Fig. 3.
5.1.1. Verification of ANFIS nonlinear mapping The verification of ANFIS for the nonlinear mapping of input – output relations of PIREM is straightforward. The historical data are divided randomly into two sets: (1) 80% of the data sets are used for training, and (2) 20% of the data sets are reserved for testing. The Matlabi Fuzzy Logic Toolbox provides functions for system training and testing. The testing error is controlled within the error goal of 5%. The maximum epochs for training are controlled within a number of 1000. When either of the above two criteria is satisfied, the system will stop the training process. Training and testing are performed for all principal cost items of each sub-module. The testing errors for the eight main project types are shown in Table 1. It is found in Table 1 that the testing errors of ANFIS for the nonlinear mapping of input – output relations are generally within the error goal, 5%, except the drainage and pavement works. The testing results are considered
Fig. 3. WICE entry page.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19 Table 1 Testing errors for the eight main project types Project type
RC bridge Drainage Earth moving Dike Retaining walls Slope Pavement Building
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Table 3 Parameters of sample drainage project
Total number of historical data
Number of training sets
Number of testing sets
Error of test (%)
160 88 220 39 51 24 30 20
135 72 180 33 40 20 24 16
25 16 40 6 11 4 6 4
2.62 7.14 4.29 2.91 2.11 2.23 8.49 3.02
acceptable for practical engineering application by the industrial partner [16]. 5.1.2. Verification of Pareto Optimum Criterion and ratio estimation Since the main objective of PIREM is to provide accurate estimation given variable unit prices of cost items, the verification experiments are planned to test the system performance under the worst scenarios. Once the system performs well in the worst scenarios, it should perform well in any conditions. Two scenarios are designed for this end: (1) all unit prices of principal items are increasing while the non-principal items are decreasing; and (2) all unit prices of principal items are decreasing while the non-principal items are increasing. Each of the above scenarios is experimented under three ranges of price variations, that is: (1) unit prices randomly varying from 0% to 5%; (2) unit prices randomly varying from 5% to 10%; and (3) unit prices randomly varying from 10% to 15%. Each of the 21 sub-modules is experimented to view the influences of unit price fluctuation on the estimation accuracy. The testing results are shown in Table 2, where, under the worst scenarios, the highest error is still around 10%. The testing results are considered by the industrial partner as insignificant, thus the proposed PIREM is verified and accepted as a reliable estimation method. 5.2. Testing of WICE
Parameter
Drainage type
Top width (m)
Net depth (m)
Value
Rectangular
0.7
0.6
simulating the data flows provided by users via Internet. The testing experiments consist of two stages: (1) system functionality testing parameters of inputs along with the related outputs of the sample projects were collected from historical database, and estimation experiments are performed via Internet to test system functionality; (2) system stability testing—multiple-user scenario is simulated by the simultaneous accessing of WICE by students in an e-Learning class. 5.2.1. System functionality testing The system functionality testing is performed for all submodules. For illustration, a sample drainage project is demonstrated here. The influential parameters of the sample drainage project are shown in Table 3. Two key functions of WICE are tested: (1) cost estimation, and (2) sensitivity analysis. For cost estimation, the values of the influential parameters are inputted into to the Parameter Input Sheet of WICE, shown in Fig. 4. In Fig. 4, three text boxes are added, which do not exist in the real WICE system, to illustrate the parameter inputs and functionality selection of WICE. After clicking on the execution bottom, the information inputted on the Parameter Input Sheet is transmitted to WICE via the data flow as shown in Fig. 2. ANFIS mapping and PIREM cost estimating are performed. The estimation results are transmitted to the Estimation Report Sheet as shown in Fig. 5. The Estimation Report Sheet in Fig. 5 shows three categories of estimation information: (1) the cost estimation—including unit construction cost and overall construction cost; (2) the parameter values inputted by the user—reminding the user of project information; and (3) detailed quantities of principal cost items along with their unit prices, for reference of the user.
The objective of system testing for WICE is to evaluate the performance of the proposed system by Table 2 Testing results of unit price fluctuation influences Scenario
Scenario I (aPUPj, bNUP,)
Scenario II (aPUP,, bNUPj)
a b
Unit price variation (%) 0–5 5 – 10 10 – 15 0–5 5 – 10 10 – 15
PUP: unit prices of principal items. NUP: unit prices of non-principal items.
Average testing error (%)
Error difference (%)
5.21 7.30 10.40 5.77 7.40 11.66
j2.65 j4.74 j7.84 j3.17 j4.84 j9.10 Fig. 4. Parameter Input Sheet of WICE.
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
Fig. 5. Estimation Report Sheet of WICE.
The user can select ‘‘sensitivity analysis’’ function, as shown in Fig. 4, to visualize the influence of parameter value changes on the overall construction cost. An example of sensitivity analysis for the sample drainage project is shown in Fig. 6. 5.2.2. System stability testing The system stability testing is performed to ensure that WICE functions normally while multiple users are accessing simultaneously. The testing experiment is performed in an e-Learning class of the Department of Construction Engineering in Chung Hua University, Hsinchu, Taiwan. In total, 34 students were accessing WICE simultaneously. The performance of WICE was monitored during the testing. It was found that WICE functioned normally with the hardware and software setup specified in Section 4.2.
6. Benefit analysis of WICE The proposed WICE system is the first-of-a-kind web-based real-time cost estimation system with learning ability to acquire estimation knowledge from historical project data. It provides both tangible and intangible benefits to the construction industrial partner. In this section, the qualitative and quantitative benefits expected from the proposed WICE are analyzed based on an internal survey by the industrial partner, CECI [17].
Moreover, the knowledge of WICE is acquired from the historical cost estimation data. Those data were stored and unused in the library of the firm in forms of design drawings and calculation reports. Such data are seldom retrieved for practical usage. This does not only waste the company’s intelligence property but also weakens the company’s competition, since the knowledge accumulated from previous projects is leaking due to the leave of experienced engineers. With the data mining technologies, the seldom-reused historical data are added with the value of their knowledge contents. Other intangible benefits may also be included, such as knowledge sharing within the company, flexibility of system usage, real-time comparison of alternatives, assistance in value engineering implementation, etc. 6.2. Quantitative benefits of WICE The proposed WICE provides not only intangible benefits described above, but the tangible benefits are also significant. According to CECI [17], the WICE system may generate revenues such as the online user membership fees—TWD 1 million (or US$28,800) per year. Moreover, the saving on the internal estimation man –hours is also expected—1 month man – hour saving for each of the 20 departments, totalling TWD 3 million (or US$86,400) per year. Therefore, the quantitative benefits of WICE are reported to be US$ 115,200 per year [16]. It certainly repays the development cost of WICE. Saving on estimation time is also significant but difficult to quantify. Usually a medium-size construction project may take an experienced estimator more than a week to finish the estimation work. With WICE, the same job can be done within half a day, depending on the similarity of the new project compared with the historical projects. Especially, for feasibility analysis, WICE outperforms any existing conceptual cost estimation systems.
6.1. Qualitative benefits of WICE The proposed WICE is the first web-based real-time cost estimation system developed in Taiwan. The industrial partner believes that the development of WICE can improve the company’s public image and reputation due to the utilization of advanced information technologies. This will also strengthen her competition in the keen construction market nowadays.
Fig. 6. Sensitivity Analysis Report Sheet of WICE.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
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7. Conclusions and recommendations
References
This paper presents the results of a joint effort by the academia and construction industrial partner on the development of a real-time web-based intelligent construction cost estimation system named WICE. In order to achieve the goals of an ideal estimation system –low maintenance cost, global accessibility, real-time response – a new conceptual cost estimation method, named PIREM, is developed and integrated with a neuro-fuzzy softcomputing technique, ANFIS, to form the proposed WICE system. The PIREM method separates unit price with the quantity of a cost item, so that the current unit price on the marketplace can be reflected in the estimation results. Detailed verification and testing of the proposed system are conducted to ensure the performance of the system and its successful application to the industry. It is found that the proposed WICE can not only achieve a higher accuracy than any existing conceptual estimation systems, but also provide a globally and alltime accessible system so that real-time cost estimation becomes possible. Moreover, the centralized knowledge base maintenance and management of WICE guarantee the users with the most updated cost estimation knowledge. Qualitative and quantitative analyses on the benefits expected from WICE are also analyzed and discussed. Both of the analyses show that significant benefits can result from WICE. Future directions of web-based applications on construction engineering and management may include business intelligence developments for design, construction, and management problems. Knowledge-based economy products generated from historical data are worthy of follow-up research.
[1] W.D. Yu, J.B. Yang, Data mining for the cost estimating of highway bridges construction with a neuro-fuzzy system, Proceedings of the 2001 ninth national conference on fuzzy theory and its applications, November 23 – 24, National Central University, Chung-li, Taiwan, 2001, pp. 437 – 442. [2] W.D. Yu, M.J. Skibniewski, Quantitative constructability analysis with a neuro-fuzzy knowledge-based multi-criterion decision support system, Autom. Constr. 8 (5) (1999) 553 – 565. [3] J.S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (3) (1993) 665 – 685. [4] N.A. Koenigseker, Parametric estimating of buildings, Cost Eng. 24 (6) (1982) 327 – 332. [5] J.S. Yu, Developing building cost estimating system using case-based reasoning approach, Master’s Thesis, Department of Civil Engineering, National Central University, Chungli, Taiwan, ROC (2001). [6] R. Koch, The 80/20 Principle—The Secret of Achieving More Less, Nicholas Brealey Publishing Ltd, London, 1997. [7] W.D. Yu, J.B. Yang, Final Report on the Development of a NeuroFuzzy Knowledge-Based System for Construction Conceptual Estimation, CECI, Taipei, Taiwan, 2002, (in Chinese). [8] S. Karshenas, Predesign cost estimating method for multistory building, J. Constr. Eng. Manag. 100 (1) (1984) 79 – 86. [9] T. Hegazy, A. Ayed, Neural network model for parametric cost estimation of highway projects, J. Constr. Eng. Manag. 124 (3) (1998) 18 – 24. [10] W.D. Yu, M.J. Skibniewski, A neuro-fuzzy computational approach to constructability knowledge acquisition for construction technology evaluation, Autom. Constr. 8 (5) (1999) 539 – 552. [11] U. Fayyad, R. Uthurusamy, Data mining and knowledge discovery in databases, Commun. ACM 39 (1996) 24 – 27. [12] S.S. Leu, C.N. Chen, S.L. Chang, Data mining for tunnel support stability: neural network approach, J. Autom. Constr. 10 (2001) 429 – 441. [13] J.S. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing— A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997, pp. 73 – 91. [14] C.T. Lin, C.S.G. Lee, Neural-network-based fuzzy logic control and decision system, IEEE Trans. Comput. 40 (12) (1991) 1320 – 1336. [15] L.X. Wang, J.M. Mendel, Back-propagation fuzzy systems as nonlinear dynamic system identifiers, IEEE Trans. Neural Netw. 3 (1992) 807 – 814. [16] W.D. Yu, C.C. Lai, WICE: a web-based intelligent cost estimator for real-time decision support, Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, October 13 – 17, 2003, Halifax, Canada, Session 10B, IEEE/WIC, 2003 (4 pp.). [17] W.L. Lee, On the commercialization of the Computerized Estimation System (CES) Development Project, CECI Internal Report, iNet, CECI, Taipei, Taiwan, September 24, 2002 (in Chinese).
Acknowledgement Most materials presented in this paper are based on the results of a research project, The Development of a NeuroFuzzy Knowledge-Based System for Construction Conceptual Estimation, which was sponsored by the China Engineering Consultants, Inc. (Taipei, Taiwan). The authors would like to express their sincere appreciation to the sponsor.
A WICE approach to real-time construction cost estimation Wen-der Yu a,*, Chien-chung Lai b, Wan-li Lee b a
Department of Construction Engineering, CHU, Hsinchu, Taiwan Information Network Department (iNet), China Engineering Consultants, Inc., Taipei, Taiwan
b
Accepted 31 January 2005
Abstract Real-time response to construction cost estimation request is crucial for construction firms to survive and grow in the industry. However, no existing construction cost estimating system fulfills this need thus far. This paper describes a joint effort, named Web-based Intelligent Cost Estimator (WICE), by the academia and the industrial partner on developing such a system. Advanced web-based intelligence techniques employed in the proposed system include WWW, neuro-fuzzy system, and data mining. The industrial partner is in charge of providing knowledge sources, including expert judgments and historical data, for conceptual cost estimation. The proposed WICE is the firstof-a-kind real-time conceptual cost estimating system in practice use. The testing results show that the proposed system provides not only a globally accessible and promptly responding means for cost estimation, but also an effective and reliable tool for real-time decision-making. D 2005 Elsevier B.V. All rights reserved. Keywords: Conceptual cost estimation; Neuro-fuzzy system; Intelligent web agent
1. Introduction Successful management of construction cost within the project budget plays a key role among the three goals of construction project management (i.e., cost within budget, schedule on time, and quality as specified). In order to achieve this major objective, accurate and reliable cost estimations should be maintained throughout the project lifecycle [1]. There have been three categories of estimations widely utilized in the area of construction management depending on the available information and required accuracy: (1) order of magnitude estimation—which is obtained with hypothetical design information and minimum site information so as to achieve the lowest accuracy; (2) conceptual estimation—with primitive design and construction site information, and to achieve low to high level of accuracy; and (3) detail estimation—with complete project design, specification, and environmental information, to achieve the highest level of accuracy. Since the third
* Corresponding author. Tel.: +886 3 5186748; fax: +886 3 5370517. E-mail address: [email protected] (W. Yu). 0926-5805/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2005.01.005
category, the detail estimation, can be achieved only when the detail designs are completed, it is not feasible in the early stages of a project, such as the conceptualizing and planning stages. As a result, the conceptual estimating methods are often adopted instead. The conceptual cost estimation during engineering planning is the most important for a construction project, since the main structural systems, major construction methods, and most construction materials are determined in that stage. However, due to the lack of detail design information during the planning phase, the accurate cost estimation becomes a difficult task for professional estimators. It was found that the estimators with more estimating experience can do better in this job than who do with less. With the emerging development of modern artificial intelligence (AI) techniques, such as neuro-fuzzy systems, the aforementioned estimating experience (knowledge) can be acquired by learning from historical examples, so that accurate estimation (compared with the detail estimation) could be obtained with very limited available project information. Unfortunately, an essential difficulty is facing the traditional conceptual cost estimation if the knowledgebased approaches are to be adopted—the unit prices of the
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
cost items are variable in the marketplace, so that the estimation knowledge learned previously may not be readily applicable in the future projects. In this paper, a novel method named ‘‘principal items ratio estimation method (PIREM) [16]’’ is employed to reflect the current unit prices of cost items. According to previous experiences, the applications of knowledge-based systems in the fields of construction engineering and management confront a major problem— the update of knowledge base and maintenance of software systems [2]. It is conceived that providing the easiest accessing means is the best way to avoid misuse of out-ofdate information. In this paper, a web-based cost estimation system is developed, which combines a neuro-fuzzy soft computing technique and the proposed PIREM conceptual cost estimating method to form an intelligent web agent (named ‘‘Web-based Intelligent Cost Estimator, WICE’’) for real-time construction cost estimations. The paper is presented in the following manner: the proposed PIREM conceptual cost estimating method is described in detail in Section 2. The application of a neuro-fuzzy soft computing technique (ANFIS [3]) for mining of conceptual cost estimation knowledge from historical data is presented in Section 3. The proposed WICE system is implemented in Section 4. System verification and testing results are shown in Section 5. In Section 6, the benefits of the proposed WICE are analyzed and discussed. Finally, findings of the research are concluded and directions for future research are recommended.
2. Principal items ratio estimation method (PIREM) Among the many conceptual estimating methods, parametric cost estimating has been widely applied in the industry for economic feasibility analysis in the early stage of a construction project. The parametric cost estimating takes important influential parameters as inputs, such as the floor area, cubic volume, bay width, etc. By statistic regression or other mapping schemes, the relations between the estimated costs and the influential parameters are established. The cost estimates of new projects are obtained by mapping inputs of parameter values based on the pre-determined mathematical relation [4]. 2.1. Problems with traditional conceptual estimating methods
13
causing this problem is that all existing analog-based estimation approaches adopt ‘‘activity cost’’ as the measure of estimates during analogizing process. This has mixed up the two elements of a construction cost item—(1) quantity of the cost item, and (2) unit price of the cost item. Even though a previous approach introduced the ‘‘overall price index’’ as a parameter for construction cost adjustments [5], the price fluctuations for individual cost items do not vary simultaneously in the same way. Adjusting the unit prices of all cost items with a single ‘‘overall price index’’ does not reflect the reality of unit price variation in the market place. A better way is to divide the two elements of the cost item and handle them separately. The quantity of a cost item could be affected by several attributes, such as the dimensions of structural design, the method of construction, the conditions of site environment, etc. The value of this element will not change as long as the same structure is to be constructed by the same method under the same environmental conditions. On the other hand, the unit price of the cost item may vary from time to time. It is reasonable to utilize the most updated unit prices for the cost items in order to reflect the real situation of the current marketplace. Another problem is that a construction project usually consists of hundreds or even thousands of cost items. It is very expensive and time consuming (if not impossible) to obtain the quantity estimates and unit prices of all items in a construction project. The Pareto Optimum Criterion (or named ‘‘80/20 Principle’’) provides a compromise for the above problem. Simply stated, the Pareto Optimum Criterion suggests that 80% of the overall project cost is determined by 20% of the cost items [6]. Therefore, instead of estimating the quantities of all cost items, only the top 20% most important cost items’ quantities are estimated, and their related unit prices are inquired. 2.2. Model of PIREM With the Pareto Optimum Criterion, almost 80% of estimation cost and time can be saved. Thus, it does not only reduce the cost but also expedite the process of estimation, and more importantly provide a feasible solution for real-time cost estimation. The selected top 20% cost items is called ‘‘Principal Items (PI)’’. The summation of quantities and unit prices of the principal cost items constitutes the Cost of Principal Items (PIC). The ratio of PIC over the overall cost is defined as the Principal Item Ratio (PIR or p). The value of p can be calculated by the following equation: l
As described in the previous section, the essential problem for all analog-based estimation approaches (such as those mentioned above) is that the unit prices of cost items are fluctuating along time. Thus, construction cost estimates obtained based on previous estimation experiences may fail due to unit price variation. The reason
ps ¼
~ UPsj Qsj j¼1 n
~ UPsl Qsi
¼
PICs ; OCs
ð1Þ
i¼1
where the numerator is the summation of the costs of all principal items, while the denominator is the summation of
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
the costs of all cost items in a project. The superscript s upon all parameters stands for the sth historical example. UPi means unit price and Q i means quantity of the ith cost item, and so forth. OC stands for overall cost. The ratio p obtained in Eq. (1) is called Principal Item Ratio (PIR) as defined above. It is found by analyzing PIRs obtained from historical cost estimation data that the PIR of a specific type of construction project usually keeps constant with very small variation [7]. Therefore, given the PIC, the OC of the new project can be recovered by the following equation: OCr ¼
PICr ; p¯
ð2Þ
where OCr is the estimate of overall cost for the new (rth) project, p¯ is the average PIR calculated from previous projects, and PICr is the cost of principal items of the new (rth) project. In Eqs. (1) and (2), the current unit prices (UPs) for the new project should be inquired from the marketplace at the moment of estimation, while the quantity, Q, and average PIR, p¯ , have to be determined by parametric estimating methods based on the pre-determined mathematical relationships. According to the literature, the mathematical relations between input parameters and the output estimate values are usually nonlinear [4,8]. In order to establish such relations, some approaches have been proposed including: (1) nonlinear regression [8], (2) artificial neural networks [9], and (3) case-based reasoning [5]. In the above, the nonlinear regression method may encounter severe convergence difficulty while dealing with more than two variables. The artificial neural network approach is good at nonlinear mapping. However, the mathematical relationships established by artificial neural networks are stored among the connection weights of the network, which is regarded as a ‘‘black box’’ to human decision makers and thus limits the value of knowledge learned [10]. The estimation error of case-based reasoning method is still as high as 15 –20% [5], which may not be accurate enough for decision-making during conceptual planning stage. In the proposed system, a neuro-fuzzy soft computing technique, ANFIS [3], is adopted to achieve more accurate estimations and provide human understandable knowledge for the estimators.
3. Mining of historical estimation data with neuro-fuzzy soft computing techniques This section describes the application of the neuro-fuzzy soft computing techniques for acquiring estimation knowledge from previous projects, so that the nonlinear relationships between the influential attributes and cost estimates can be established. The technology for extracting useful information from historical data for decision makers is named data mining (DM) [1,11]. Here, the generic process of data mining
is described and followed by the application of a neuro-fuzzy approach for data mining. 3.1. Process of data mining Data mining was defined as the application of automated knowledge acquisition methods for generation of useful knowledge via organization and analysis of raw data [11]. The procedure for data mining implementation consists of five steps [12]: (1) objective determination; (2) data preparation; (3) data transformation; (4) data mining; and (5) result analysis. Two key issues for data mining are [1]: (1) the accuracy of knowledge acquisition, and (2) the format knowledge representation. In this paper, a neurofuzzy system named ANFIS is adopted for the purpose of data mining for cost estimation primarily due to the excellent learning ability and explicit knowledge representation of neuro-fuzzy systems. 3.2. Neuro-fuzzy soft computing techniques Neuro-fuzzy system is a branch of artificial intelligence (AI) techniques, which combines the merits of artificial neural networks (ANN) and fuzzy inference systems (FIS). A basic structure of an FIS is comprised of three components [13]: (1) a rule base, which stores a bunch of fuzzy if – then rules; (2) a database, which defines the membership functions used in the fuzzy rules; and (3) a reasoning mechanism, which performs fuzzy inference upon the rules and given facts to derive a reasoning output. 3.2.1. Fuzzy inference systems (FIS) There are three major types of FIS [13]: Mamdani, Sugeno, and Tsukamoto. Following briefly is a review of the three FIS. Mamdani FIS is a general type of FIS that adopts ‘‘max’’ and ‘‘algebraic product’’ for fuzzy T-norm and T-conorm operations. A typical fuzzy if – then rule for Mamdani FIS is shown in Eq. (3): Rk : If xl is Ak1 and . . . and xp is Akp ; then y is Bk ;
ð3Þ
where R k means the kth fuzzy rule; Aki and B k represent T fuzzy linguistic variables; x¯ ¼ x1 ; x2 ; . . . ; xp ˛Rp and y˛R are the inputs and output of the kth fuzzy rule. Sugeno FIS is a special type of FIS that adopts a crisp function in the consequence of a fuzzy decision rule. A fuzzy if – then rule for Sugeno FIS is shown in Eq. (4): Rk : If x1 is Akl and . . . and xp is Akp ; then y is fk ðx1 ;x2 ;. . .;xp Þ;
ð4Þ
where R k , Aki , B k , x¯ = (x 1,x 2,. . .,x p )T and y are defined similarly as in Eq. (1), while f k (x 1,x 2,. . .,x p ) is a polynomial taking on x 1,x 2,. . .,x p and is used to define the consequence of a fuzzy decision rule.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
Tsukamoto FIS is also a special type of FIS that adopts a monotonical membership function in the consequent part of a fuzzy decision rule. A typical fuzzy decision rule for Tsukamoto FIS is shown in Eq. (5): Rk : If x1 is Ak1 andN and xp is Akp ; then y is Ck :
ð5Þ
where R k , Aki , B k , (x 1,x 2,. . .,x p )T, and y are defined similarly as in Eq. (1), while C k are monotonical membership functions used to describe the consequent part of a fuzzy decision rule. 3.2.2. Learning schemes for FIS While applying to data mining, the three FIS mentioned above need to be equipped with ‘‘learning abilities’’ so that they are able to ‘‘mine’’ knowledge from raw data. Several schemes can be used for constructing a neuro-fuzzy system such as: (1) FALCON proposed by Lin and Lee [14], (2) ANFIS proposed by Jang [3], and (3) back-propagation fuzzy system proposed by Wang and Mendel [15]. In this paper, the ANFIS scheme is adopted for constructing a Sugeno FIS. For details of ANFIS neuro-fuzzy learning scheme, the readers are referred to excellent text by Jang et al. [13].
4. Implementation of Web-based Intelligent Cost Estimator (WICE) 4.1. Conceptual planning of WICE While the integration of PIREM cost estimation approach and ANFIS neuro-fuzzy system provides the functions of an intelligent cost estimating system, the update of the knowledge base and the maintenance of the software system are tedious routines and may cause significant cost burden for the system developer. The best policy should minimize the aforementioned routines and centralize the knowledge base management tasks. An intuitive solution is to implement the proposed system on the web. The conceptual planning of the proposed WICE system is shown in Fig. 1.
WICE Users around the world Parameters inputs
Project A
15
Three critical concerns for planning WICE are: (1) lowest maintenance costs—the system should adopt centralized data and knowledge base management; (2) global and all-time accessibility—the system should adopt webbased application with Internet connection; and (3) real-time response—the system should minimize the online calculation requirements. To meet all three requirements described above, the proposed WICE system is planned as an Internet-based application program that resides on the server of the Internet Service Provider (ISP), so that the system is maintained solely by the ISP and the user around the world can access WICE via the World Wide Web without any time or location restrictions. To reduce the online calculation time, the ANFIS network mapping is readily suitable for this end. 4.2. Framework and data processing of WICE The Matlabi 6.3 with Web Server and Fuzzy Logic Toolboxes is adopted for WICE development. The platform selected for WICE implementation consists of: (1) PC CPU: Pentium IV 1.5G; (2) OS: Windows 2000 Server; and (3) SRAM: 1.0 G. Since the ANFIS trainings can be done offline, the CPU speed for the web server may be not demanding. However, broader bandwidth for transmission is required if the number of simultaneous accesses or need for graphic transmissions is large. The framework and data flows of WICE are depicted in Fig. 2. As depicted in Fig. 2, Client (User) A submits parameter inputs (including influential attributes and unit prices) and job requests to WICE by filling out the Parameter Input Sheet (see Fig. 4). The WICE then transmits the input data to Matlab main system via the interface and web server programs. The Matlab main system consults related Matlab programs and performs the required calculations (including ANFIS network mapping), and then the results (including estimation results and relevant graphics) are generated. The numeric data (resulted estimates) and graphics are handled separately hereafter. The numeric data are transmitted backward via web server and interface programs to the responding form (Estimation Report Sheet; see Fig. 5) of the client. Meanwhile, the generated graphics (if applicable) are loaded on the responding form (such as Sensitivity Analysis Report Sheet; see Fig. 6). Thus the request of Client A is completely served. Multiple requests by different users are served simultaneously via the similar process for Client A described above, while the buffer of memory (i.e., capacity of RAM) for storing intermediate data is required as the number of simultaneous users increases. The intermediate data are handled by the web server and interface programs so that no conflict occurs while multiple users are accessing WICE. 4.3. System deployment
Project B
Project C
Fig. 1. Conceptual planning of the proposed system.
The WICE system is deployed to meet the practical requirements of the industrial partner. In total, eight main
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
HTML respond form
HTML request form
W I C E
5. Verification and testing
Client B
Client A
HTML request form
HTML respond form
In order to ensure the performance of the proposed system, verification and testing are performed for PIREM and WICE, respectively. 5.1. Verification of PIREM cost estimation method
HTTP Daemon
The proposed PIREM is founded on several predeveloped principles or methods: (1) Pareto Optimum Criterion (80/20 principle); (2) ratio estimation; and (3) nonlinear mapping of ANFIS. All these principles and methods should be verified under the given conditions that are confronted in construction cost estimation to show their applicability.
Interface program Generated graphics
Web Server Matlab Main System
Data warehouse
Fuzzy Logic Toolbox
Knowledge base
Fig. 2. Framework and data flows of WICE.
modules are developed according to the type of construction projects, while each module consists of one to five sub-modules to deal with special works within each project type. The eight main project types are: (1) reinforced concrete bridges; (2) drainage works; (3) Earth moving; (4) dike construction; (5) retaining walls; (6) slope protection works; (7) pavement works; and (8) building construction. In total, 21 sub-modules are developed in WICE. The entry page of WICE system is shown in Fig. 3.
5.1.1. Verification of ANFIS nonlinear mapping The verification of ANFIS for the nonlinear mapping of input – output relations of PIREM is straightforward. The historical data are divided randomly into two sets: (1) 80% of the data sets are used for training, and (2) 20% of the data sets are reserved for testing. The Matlabi Fuzzy Logic Toolbox provides functions for system training and testing. The testing error is controlled within the error goal of 5%. The maximum epochs for training are controlled within a number of 1000. When either of the above two criteria is satisfied, the system will stop the training process. Training and testing are performed for all principal cost items of each sub-module. The testing errors for the eight main project types are shown in Table 1. It is found in Table 1 that the testing errors of ANFIS for the nonlinear mapping of input – output relations are generally within the error goal, 5%, except the drainage and pavement works. The testing results are considered
Fig. 3. WICE entry page.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19 Table 1 Testing errors for the eight main project types Project type
RC bridge Drainage Earth moving Dike Retaining walls Slope Pavement Building
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Table 3 Parameters of sample drainage project
Total number of historical data
Number of training sets
Number of testing sets
Error of test (%)
160 88 220 39 51 24 30 20
135 72 180 33 40 20 24 16
25 16 40 6 11 4 6 4
2.62 7.14 4.29 2.91 2.11 2.23 8.49 3.02
acceptable for practical engineering application by the industrial partner [16]. 5.1.2. Verification of Pareto Optimum Criterion and ratio estimation Since the main objective of PIREM is to provide accurate estimation given variable unit prices of cost items, the verification experiments are planned to test the system performance under the worst scenarios. Once the system performs well in the worst scenarios, it should perform well in any conditions. Two scenarios are designed for this end: (1) all unit prices of principal items are increasing while the non-principal items are decreasing; and (2) all unit prices of principal items are decreasing while the non-principal items are increasing. Each of the above scenarios is experimented under three ranges of price variations, that is: (1) unit prices randomly varying from 0% to 5%; (2) unit prices randomly varying from 5% to 10%; and (3) unit prices randomly varying from 10% to 15%. Each of the 21 sub-modules is experimented to view the influences of unit price fluctuation on the estimation accuracy. The testing results are shown in Table 2, where, under the worst scenarios, the highest error is still around 10%. The testing results are considered by the industrial partner as insignificant, thus the proposed PIREM is verified and accepted as a reliable estimation method. 5.2. Testing of WICE
Parameter
Drainage type
Top width (m)
Net depth (m)
Value
Rectangular
0.7
0.6
simulating the data flows provided by users via Internet. The testing experiments consist of two stages: (1) system functionality testing parameters of inputs along with the related outputs of the sample projects were collected from historical database, and estimation experiments are performed via Internet to test system functionality; (2) system stability testing—multiple-user scenario is simulated by the simultaneous accessing of WICE by students in an e-Learning class. 5.2.1. System functionality testing The system functionality testing is performed for all submodules. For illustration, a sample drainage project is demonstrated here. The influential parameters of the sample drainage project are shown in Table 3. Two key functions of WICE are tested: (1) cost estimation, and (2) sensitivity analysis. For cost estimation, the values of the influential parameters are inputted into to the Parameter Input Sheet of WICE, shown in Fig. 4. In Fig. 4, three text boxes are added, which do not exist in the real WICE system, to illustrate the parameter inputs and functionality selection of WICE. After clicking on the execution bottom, the information inputted on the Parameter Input Sheet is transmitted to WICE via the data flow as shown in Fig. 2. ANFIS mapping and PIREM cost estimating are performed. The estimation results are transmitted to the Estimation Report Sheet as shown in Fig. 5. The Estimation Report Sheet in Fig. 5 shows three categories of estimation information: (1) the cost estimation—including unit construction cost and overall construction cost; (2) the parameter values inputted by the user—reminding the user of project information; and (3) detailed quantities of principal cost items along with their unit prices, for reference of the user.
The objective of system testing for WICE is to evaluate the performance of the proposed system by Table 2 Testing results of unit price fluctuation influences Scenario
Scenario I (aPUPj, bNUP,)
Scenario II (aPUP,, bNUPj)
a b
Unit price variation (%) 0–5 5 – 10 10 – 15 0–5 5 – 10 10 – 15
PUP: unit prices of principal items. NUP: unit prices of non-principal items.
Average testing error (%)
Error difference (%)
5.21 7.30 10.40 5.77 7.40 11.66
j2.65 j4.74 j7.84 j3.17 j4.84 j9.10 Fig. 4. Parameter Input Sheet of WICE.
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W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
Fig. 5. Estimation Report Sheet of WICE.
The user can select ‘‘sensitivity analysis’’ function, as shown in Fig. 4, to visualize the influence of parameter value changes on the overall construction cost. An example of sensitivity analysis for the sample drainage project is shown in Fig. 6. 5.2.2. System stability testing The system stability testing is performed to ensure that WICE functions normally while multiple users are accessing simultaneously. The testing experiment is performed in an e-Learning class of the Department of Construction Engineering in Chung Hua University, Hsinchu, Taiwan. In total, 34 students were accessing WICE simultaneously. The performance of WICE was monitored during the testing. It was found that WICE functioned normally with the hardware and software setup specified in Section 4.2.
6. Benefit analysis of WICE The proposed WICE system is the first-of-a-kind web-based real-time cost estimation system with learning ability to acquire estimation knowledge from historical project data. It provides both tangible and intangible benefits to the construction industrial partner. In this section, the qualitative and quantitative benefits expected from the proposed WICE are analyzed based on an internal survey by the industrial partner, CECI [17].
Moreover, the knowledge of WICE is acquired from the historical cost estimation data. Those data were stored and unused in the library of the firm in forms of design drawings and calculation reports. Such data are seldom retrieved for practical usage. This does not only waste the company’s intelligence property but also weakens the company’s competition, since the knowledge accumulated from previous projects is leaking due to the leave of experienced engineers. With the data mining technologies, the seldom-reused historical data are added with the value of their knowledge contents. Other intangible benefits may also be included, such as knowledge sharing within the company, flexibility of system usage, real-time comparison of alternatives, assistance in value engineering implementation, etc. 6.2. Quantitative benefits of WICE The proposed WICE provides not only intangible benefits described above, but the tangible benefits are also significant. According to CECI [17], the WICE system may generate revenues such as the online user membership fees—TWD 1 million (or US$28,800) per year. Moreover, the saving on the internal estimation man –hours is also expected—1 month man – hour saving for each of the 20 departments, totalling TWD 3 million (or US$86,400) per year. Therefore, the quantitative benefits of WICE are reported to be US$ 115,200 per year [16]. It certainly repays the development cost of WICE. Saving on estimation time is also significant but difficult to quantify. Usually a medium-size construction project may take an experienced estimator more than a week to finish the estimation work. With WICE, the same job can be done within half a day, depending on the similarity of the new project compared with the historical projects. Especially, for feasibility analysis, WICE outperforms any existing conceptual cost estimation systems.
6.1. Qualitative benefits of WICE The proposed WICE is the first web-based real-time cost estimation system developed in Taiwan. The industrial partner believes that the development of WICE can improve the company’s public image and reputation due to the utilization of advanced information technologies. This will also strengthen her competition in the keen construction market nowadays.
Fig. 6. Sensitivity Analysis Report Sheet of WICE.
W. Yu et al. / Automation in Construction 15 (2006) 12 – 19
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7. Conclusions and recommendations
References
This paper presents the results of a joint effort by the academia and construction industrial partner on the development of a real-time web-based intelligent construction cost estimation system named WICE. In order to achieve the goals of an ideal estimation system –low maintenance cost, global accessibility, real-time response – a new conceptual cost estimation method, named PIREM, is developed and integrated with a neuro-fuzzy softcomputing technique, ANFIS, to form the proposed WICE system. The PIREM method separates unit price with the quantity of a cost item, so that the current unit price on the marketplace can be reflected in the estimation results. Detailed verification and testing of the proposed system are conducted to ensure the performance of the system and its successful application to the industry. It is found that the proposed WICE can not only achieve a higher accuracy than any existing conceptual estimation systems, but also provide a globally and alltime accessible system so that real-time cost estimation becomes possible. Moreover, the centralized knowledge base maintenance and management of WICE guarantee the users with the most updated cost estimation knowledge. Qualitative and quantitative analyses on the benefits expected from WICE are also analyzed and discussed. Both of the analyses show that significant benefits can result from WICE. Future directions of web-based applications on construction engineering and management may include business intelligence developments for design, construction, and management problems. Knowledge-based economy products generated from historical data are worthy of follow-up research.
[1] W.D. Yu, J.B. Yang, Data mining for the cost estimating of highway bridges construction with a neuro-fuzzy system, Proceedings of the 2001 ninth national conference on fuzzy theory and its applications, November 23 – 24, National Central University, Chung-li, Taiwan, 2001, pp. 437 – 442. [2] W.D. Yu, M.J. Skibniewski, Quantitative constructability analysis with a neuro-fuzzy knowledge-based multi-criterion decision support system, Autom. Constr. 8 (5) (1999) 553 – 565. [3] J.S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (3) (1993) 665 – 685. [4] N.A. Koenigseker, Parametric estimating of buildings, Cost Eng. 24 (6) (1982) 327 – 332. [5] J.S. Yu, Developing building cost estimating system using case-based reasoning approach, Master’s Thesis, Department of Civil Engineering, National Central University, Chungli, Taiwan, ROC (2001). [6] R. Koch, The 80/20 Principle—The Secret of Achieving More Less, Nicholas Brealey Publishing Ltd, London, 1997. [7] W.D. Yu, J.B. Yang, Final Report on the Development of a NeuroFuzzy Knowledge-Based System for Construction Conceptual Estimation, CECI, Taipei, Taiwan, 2002, (in Chinese). [8] S. Karshenas, Predesign cost estimating method for multistory building, J. Constr. Eng. Manag. 100 (1) (1984) 79 – 86. [9] T. Hegazy, A. Ayed, Neural network model for parametric cost estimation of highway projects, J. Constr. Eng. Manag. 124 (3) (1998) 18 – 24. [10] W.D. Yu, M.J. Skibniewski, A neuro-fuzzy computational approach to constructability knowledge acquisition for construction technology evaluation, Autom. Constr. 8 (5) (1999) 539 – 552. [11] U. Fayyad, R. Uthurusamy, Data mining and knowledge discovery in databases, Commun. ACM 39 (1996) 24 – 27. [12] S.S. Leu, C.N. Chen, S.L. Chang, Data mining for tunnel support stability: neural network approach, J. Autom. Constr. 10 (2001) 429 – 441. [13] J.S. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing— A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997, pp. 73 – 91. [14] C.T. Lin, C.S.G. Lee, Neural-network-based fuzzy logic control and decision system, IEEE Trans. Comput. 40 (12) (1991) 1320 – 1336. [15] L.X. Wang, J.M. Mendel, Back-propagation fuzzy systems as nonlinear dynamic system identifiers, IEEE Trans. Neural Netw. 3 (1992) 807 – 814. [16] W.D. Yu, C.C. Lai, WICE: a web-based intelligent cost estimator for real-time decision support, Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, October 13 – 17, 2003, Halifax, Canada, Session 10B, IEEE/WIC, 2003 (4 pp.). [17] W.L. Lee, On the commercialization of the Computerized Estimation System (CES) Development Project, CECI Internal Report, iNet, CECI, Taipei, Taiwan, September 24, 2002 (in Chinese).
Acknowledgement Most materials presented in this paper are based on the results of a research project, The Development of a NeuroFuzzy Knowledge-Based System for Construction Conceptual Estimation, which was sponsored by the China Engineering Consultants, Inc. (Taipei, Taiwan). The authors would like to express their sincere appreciation to the sponsor.