رویکرد مدل سازی محصول هزینه برای توسعه یک سیستم پشتیبانی تصمیم گیری برای انتخاب مواد سقف سازی بهینه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|5774||2012||15 صفحه PDF||سفارش دهید||8746 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 8, 15 June 2012, Pages 6857–6871
Selection of optimal roofing materials is very important but it is a complex and onerous task as varieties of materials are available for housing roof construction. In order to select suitable materials, an extensive range of criteria would need to be considered. This paper presents the framework and the development of a knowledge-based decision support system for material selection implemented in roofing material selection domain, called ‘Knowledge-based Decision Support system for roofing Material Selection and cost estimating’ (KDSMS). It was developed to facilitate the selection of optimal materials for different roof sub elements. The system consists of a database and knowledge base that is equipped with an inference engine. The former is used to store different types of roofing materials with assigned attribute values. The later is used to hold qualitative and quantitative knowledge which were collected from domain experts and other technical literatures such as building regulations, price guide book and product catalogues. The proposed system employs the TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method to solve materials selection and optimisation problem. This study utilised the available roofing materials in the UK housing market in developing the system reported. The main contribution of the developed system is that it provides a tool for the architects, quantity surveyors or self house builder to select optimal materials from a wide array of possibilities for different roof sub elements and also to estimate the conceptual cost for the roof element in the early stage of building design.
Different types of materials and technologies are available for building design and construction while new materials and advanced technologies are continuously being introduced into the market (Wong & Li, 2008). The selection of materials is a complex procedure and it is difficult to match materials based on design requirements (Ashby, Brechet, Cebon, & Salvo, 2004). Materials are generally selected from the existing catalogues of materials and traditionally experts apply trial and error methods or use experiences to choose new materials or materials having better performance (Shanian & Savadogo, 2006). It is acknowledged that the selection of appropriate materials may reduce the energy consumption and maintenance cost of buildings (Papadopoulos & Giama, 2007). As buildings are responsible for significant impact on the environment, eco-friendly materials are becoming popular for housing construction (Hymers, 2006). Moreover, there is an increasing demand for sustainable and energy-efficient construction (UNEP, 2001) and the use of environmental friendly materials (Chan and Tong, 2007 and Roaf et al., 2007). However, there is a lack of public awareness about sustainable and energy-efficient construction and these issues are unfamiliar to many architects, engineers, and contractors (UNEP, 2001). Evidences from literature suggest that the building owners and clients tend to emphasise the initial cost rather than operating cost (Wilson et al., 1998). Karolides, 2006 and Woolley, 2006 emphasised that the amount of energy needed can be reduced by using high performance and extra insulation, which is the easiest and least expensive way to solve energy problem. Architects or cost engineers need to consider several factors in order to select optimum materials to meet clients’ requirements. In order to solve this problem of material selection in a way that meets design and clients’ requirements and results in sustainable construction, it is required to analyse and synthesis a multitude of criteria (Rahman et al., 2009a and Rahman et al., 2009b). Different approaches regarding materials selection have been devised for different purposes. For instance, knowledge-based or expert systems have been developed to select materials for different purposes. Bullinger, Warschat, and Fischer (1991) proposed a knowledge-based system to select optimal materials for construction with fibre-reinforced composite materials. Soronis (1992) proposed a method for the selection of roofing materials where several factors have been taken into consideration to assess durability only. Chen, Sun, and Hwang (1995) developed an intelligent system for composite material selection in structural design. Mahmoud, Aref, and Al-Hammad (1996) developed a method for selection of finishing materials that covered floors, walls and ceilings. Mohamed and Celik (1998) proposed a knowledge-based method regarding materials selection and cost estimating for a residential building where users could choose their preferred one from a list of materials without evaluation and synthesis of multiple design criteria and client requirements. Instead of expert or knowledge-based systems, Perera and Fernando (2002) proposed a cost modelling system for roofing material selection where several factors are identified and considered in the selection process. Chan and Tong (2007) acknowledged the fact that the decision to select appropriate material is not simply a consideration of cost and materials properties but also there is a need to consider environmental impacts. It is identified that the selection of material is a key issue for the environment (Chan & Tong, 2007) and the choice of material is the optimal way to achieve the energy efficient construction of a building (Krope & Goricanec, 2009). In view of the foregoing, the design team needs to consider several factors in order to select the more suitable materials to meet clients’ requirements. In order to solve this problem of material selection in a way that meets the requirement of the design team and those of the construction clients and results in sustainable construction and cost effective solutions, it is required to simultaneously analyse and synthesise multitudes of criteria in order to achieve an optimum solution. It is identified that few decision support systems have been devised for roofing materials selection but the proposed systems do not have the facility to select the appropriate materials by evaluating them with respect to the multitudes of criteria to be considered in order to meet the clients’ expectations. Some systems attempt to solve the problem of materials selection by adopting rule-based knowledge representation in terms of IF-THEN rules. However, it is difficult to rank the most suitable materials using conditional expressions. This clearly indicates a research gap with respect to selecting the optimum roofing materials by analysing and synthesising a multitude of design and client’s requirements that are both cost effective and sustainable. In order to fill this gap, it is necessary to develop a system that has the capability of simultaneously evaluating multiple criteria in the optimisation of materials selection for roof design. Hence, this research aims to bridge the current knowledge gap by developing a knowledge-based decision support system, called Knowledge-based Decision support System for roofing Material Selection and cost estimating (KDSMS). Its aim is to optimise the selection of roofing materials and model the associated cost for the roof element at an early stage of building design. This system adopts the Technique Of ranking Preferences by Similarity to the Ideal Solution (TOPSIS) method to solve Multi Criteria Decision Making (MCDM) problems. The advantage of this method is its efficiency and simplicity to use and the ability to rank the materials indisputably (Shanian & Savadogo, 2006). Architects, Cost Engineers, Quantity Surveyors and self builders are the potential users of this system. It has the potential of assisting them in selecting optimal materials from the list of alternatives based on the level of importance of the criteria set by them. In addition, the system estimates the cost of the optimal materials selected to determine the budget. This system also can be used to educate the users about new materials by providing relevant information.
نتیجه گیری انگلیسی
This paper presents the KDSMS system, a Knowledge-based Decision Support system for the selection of optimal Materials for building design. The system uses product cost modelling techniques and the MCDM technique of TOPSIS for optimal materials selection and has been implemented as a prototype system for optimal roofing material selection and cost modelling. The KDSMS system uses an architecture that integrates the knowledge base with the Oracle database system. This system resolves MCDM problem by identifying a multitude of criteria involved in roofing materials selection and evaluating them in the selection procedure. The system enables new material information to be added and the database can be updated easily. It also enables the updating of prices for both time factors and location using the tender price and location indices obtained from the BCIS. It also provides sustainable rating for materials so that it can facilitate the effective selection of sustainable and innovative building materials thereby facilitating the reduction of carbon footprint. In addition, the system provides an approximate cost estimate for roof and its sub elements based on the optimal materials selected. Optimal materials are always preferred not only for environmental reasons but also for cost effectiveness and ease of maintenance. More sustainable materials contribute to the sustainable construction and help the environment by reducing the carbon footprint. This requires the need to simultaneously consider a multitude of criteria in selecting optimal materials with higher sustainability. Moreover, new innovative materials are frequently introduced to the market, but may not be used due to lack of information and experience of the designers or clients (e.g. self-build). Therefore, building designers or inexperienced users such as self builders are often faced with the problem of information overload and pressures on innovative and sustainable design. This system employs the use of a knowledge-based system to overcome this problem. The materials along with the values of properties are stored in a database; which allows quick and efficient retrieval of appropriate product details and the values of properties to evaluate performance when required. Several systems have attempted to solve this problem but none have successfully utilised the multi criteria decision making (MCDM) techniques in roofing materials selection within the housing design domain. This system fills this gap and proposes a knowledge-based model as the decision making support tool to provide optimal product/material selection and estimate of approximate cost from the early conceptual stage of design. The developed system is not without its limitations. It is limited to pitched roofs of housing projects and their related materials. As such, it did not include flat roofs and associated materials for flat roof construction. In addition, the study did not include qualitative and subjective criteria such as weather resistance, sound resistance, strength and stability, fire resistance and security, and associated building regulations, which may influence material selection. The reason for this exclusion in this present research is to avoid the use of subjective criteria in the present prototype that was developed. It is also noted that a building may have several roofs with different angle of pitch and roof spans. However, there is a limitation in this study in that the KDSMS system can handle a single roof with a roof span and angle of pitch and this limitation hinders the selection of materials and estimating of cost for multiple roofs of a building. However, this system has several advantages. Firstly, with the aid of this decision support system, the architects, quantity surveyors and home owners are made more aware in a user friendly manner about the multitudes of selection criteria to be considered in the selection of roofing materials. Secondly, the knowledge of material selection method through the use of multiple criteria decision making would assist the Cost engineers, Quantity Surveyors and Architects to evaluate and select optimal materials from the vast array of possibilities to meet specific requirements. Thirdly, the cost modelling facility offered by the system would assist the Quantity Surveyors and Architects to estimate roofing cost with new materials from an early stage of building design. This can save enormous time and a significant cost of roof construction. Moreover, they are able to overcome the information overload which might prevent them from the selection of suitable materials. Finally, the system enables the evaluation and choice of optimal materials for the construction of sustainable and energy-efficient buildings which can contribute to the reduction of carbon foot print. The optimal materials will require less energy, thus the end-users can save a significant energy cost. The approach adopted in this research is generic to all other building elements; as such further research needs to be carried out to cover flat roof and other elements of a building in order to facilitate the effective use of innovative and sustainable building materials and technologies. This research can be effectively expanded to other building types such as educational buildings, retail, industrial buildings, health, commercial, hospital and sport centre. It is suggested that further research would be necessary to consider the inclusion of qualitative and other subjective criteria and associated building regulations which influence material selection. In addition, further research also needs to be conducted to estimate the cost for multi roofs of a building, and to obtain relevant data on life cycle assessment for a roof such as energy cost, cleaning cost among other running cost to evaluate materials more efficiently. The research can further be expanded and implemented worldwide with modifications to account for specific regional location factors and regulations. Moreover, the current system and database can be expanded and implemented as a commercial package and transformed to fully web enabled system.