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Supporting the technical management of residential buildings in the process of their exploitation
Abstract
By analyzing the individual stages of the building's life cycle, it can be easily concluded that the building's exploitation process is the longest and at the same time it is the justification for the construction project related to the construction of this building. In the course of the building's exploitation, various phenomena occur that affect its condition and thus the possibility of unlimited use. These are natural phenomena, as well as phenomena derived from external influences, which often lead to deterioration of the building's condition, or even its degradation. In response to these phenomena, maintenance, renovation and modernization activities are undertaken. Technical management is related to the identification of these phenomena, programming of adequate measures and their implementation. The conducted analysis of the results of the survey in the group of property managers allows to state categorically that the process of technical management is relatively little supported by IT tools and is still based on individual analysis and often intuitive actions. The article presents the possibilities of applying an innovative approach in the acquisition and collection of information about the technical condition of buildings, indicating the legitimacy of standardizing information forms and using them in building a database of cases of the CBR (case based reasoning) inference system. © 2021. M. Gajzler. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (CC BY-NC-ND 4.0, https://creativecommons.org/licenses/by-nc-nd/4.0/), which per-mits use, distribution, and reproduction in any medium, provided that the Article is properly cited, the use is non-commercial, and no modifications or adaptations are made.
... Share documents among CS companies (e.g., the e-Cognos project) [50,51,60] Search for online articles or technical documents to guide construction practice [67] Identify CAD drawings/documents for design reuse [46,90] Find technical specifications for engineering problems (e.g., concrete cracks) [84,94,123], building maintenance [124], and remedial actions for incidents [101] Information retrieval within documents ...
... Train ML models to determine if a document should be retrieved given text and meta-features of the query [48] CBR Retrieve the most similar documents stored in CBR databases [101,124] Other Apply path finding (e.g., Dijkstra) to IFC/IFD (tree structures) to retrieve BIM information by mapping extracted entities to IFC/ IFD items [49] ...
Unstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements , this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques.
Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.
This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.
The article presents the problem of the analysis of post-inspection protocols of buildings in order to obtain the necessary data and information for the construction of a system supporting the technical management of residential buildings. The attention was primarily focused on the document itself - a protocol of control and automated technique of obtaining data and information from this document - text mining technique. Problems appearing in the analysis and the potential for their removal were indicated. The conclusions formulated relate to improvement of the process of conducting the review and formulating the post-control protocol in the aspect of its use in automated data collection and information transfer involving state of buildings.
The idea of sustainable development and the resulting environmentally friendly attitudes are increasingly used in construction projects. Designing in accordance with the principles of sustainable development has an impact on the costs of construction works. The authors of this paper proposed an approach to estimate the costs of sports field construction using the Case Based Reasoning method. In their analysis, they distinguished 16 factors that affect the cost of a construction project and are possible to already be described at an early stage of its preparation. The original elements of the work include: consideration of such environmental factors as the environmental impact of the building, materials used, the impact of the facility on the surroundings affecting the amount of implementation costs and development of own database containing 143 construction projects that are related to sports fields. In order to calculate the similarity of cases, different calculation formulas were applied depending on the type of data (quantitative, qualitative, uncertain, no data). The obtained results confirmed that the CBR method based on historical data and using criteria related to sustainable development may be useful in cost estimation in the initial phase of a construction project. Its application to the calculation of the costs that are related to the implementation of sports fields generates an error of 14%, which is a very good result for initial calculations. In the short run, such factors as the impact of the object and the type of materials that are used from the perspective of their influence on the environment may be decisive as far as the costs determined in the life cycle of the building are concerned, as well as the lowest costs of the building construction ensuring the appropriate quality and respect for the environment.
Operators’ work order descriptions in computerized maintenance management systems (CMMS) represent an untapped opportunity to benchmark a facility’s maintenance and operation performance. However, it is challenging to carry out analytics on these large and amorphous databases. This paper puts forward a text-mining method to extract information about failure patterns in building systems and components from CMMS databases. The method is executed in three steps. Step 1 is pre-processing to convert work order descriptions into a mathematical form that lends itself to a quantitative lexical analysis. Step 2 is clustering to focus on interesting sections of a CMMS database that contain work orders about failures in building systems and components – rather than less interesting routine maintenance and inspection activities. Step 3 is association rule-mining to identify the coexistence tendencies among the terms of cluster of interest (e.g. coexistence of the terms ‘radiator’ and ‘leak’). This text-mining method is demonstrated by using two data sets. One data set was from a central heating and cooling plant with four boilers and five chillers; the other data set was from a cluster of 44 buildings. The results provide insights into per equipment breakdown of failure events, top system and component-level failure modes, and their occurrence frequencies.
Huge amounts of data are generated daily during the operation and maintenance (O&M) phase of buildings. These accumulated data have the potential to provide deep information that can help improve facility management. Building Information Model/Modeling (BIM) technology has proven potential in O&M management in some studies, making it possible to store massive data. However, the complex and non-intuitive data records, as well as inaccurate manual inputs, raise difficulties in making full use of information in current O&M activities. This paper aims to address these problems by proposing a BIM-based Data Mining (DM) approach for extracting meaningful laws and patterns, as well as detecting improper records. In this approach, the BIM database is first transformed into a data warehouse. After that, three DM methods are combined to find useful information from the BIM. Specifically, the cluster analysis can find relationships of similarity among records, the outlier detection detects manually input improper data and keeps the database fresh, and the improved pattern mining algorithm finds deeper logic links among records. Particular emphasis is put on introducing the algorithms and how they should be used by building managers. Hence, the value of BIM is increased based on rules, extracted from data of O&M phase that appear irregular and disordered. Validated by an integrated on-site practice in an airport terminal, the proposed DM methods are helpful in prediction, early warning, and decision making, leading to the improvements of resource usage and maintenance efficiency during the O&M phase.
Communication and information management are among the key areas of management operations at every stage of a construction project life cycle. The use of innovative digital technologies and tools for conducting such operations may significantly improve the preparation and execution of a construction project and it enables the preparation of a collection of information required to efficiently use the structure. The aim of this article is to present the results of the latest research related to the application of digitization in the Facility Management based on a critical literature review of 2010-2016. The article investigates the potential of Building Information Modelling to integrate data for the building lifecycle, providing information and decision support to facility management and maintenance. The results of the implementation of digital techniques for the purpose of smart Facility Management were discussed on the basis of a case study of The Edge office building in Amsterdam. The article also suggests possible directions for further research related to digitization in property management and its impact on the process of implementing the concept of intelligent and sustainable construction engineering.
Despite the ubiquity of textual data, so far, few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This paper aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are: 1) dimensionality reduction, 2) distance and similarity computing, 3) clustering, 4) topic modelling, and 5) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the manuscript by illustrating the text mining process in a job analysis setting using a dataset comprised of job vacancies.
With the rapid advancement of sensing and computing technology and the wide adoption of mobile computing, the construction industry has faced a rise in the amount of information and data generated during the lifecycle of the construction project. To deal with a large variety of project data and information to support efficient and effective decision making, visualization, information modeling, and simulation (VIMS) has become critical in the development of capital facilities and infrastructures. The objective of this paper is to identify and investigate grand challenges in VIMS for the construction industry, to assist the academic and industry communities in establishing a future research agenda to solve VIMS challenges. In particular, 17 VIMS grand challenges were identified by an expert task force in the VIMS committee of the ASCE Computing and Information Technology Division, and then VIMS experts in the civil and construction areas from both academia and industry participated in a survey to assess the identified challenges, examine the relative importance of the identified challenges, and investigate current practices and future directions of VIMS. The survey results indicate that several knowledge gaps regarding VIMS challenges between academia and industry still exist, and it is the contention of this research that these particular gaps need to be addressed in future research. These research directions apply to technical issues and sociological/cultural/organizational issues in VIMS challenges. The major contribution of this paper is its claim that the provision of shared views on VIMS challenges lays a firm foundation in which collaborative actions between academia and industry can take place, which will, in turn, advance VIMS for the construction industry. By incorporating the whole project lifecycle, not only project execution but also planning, operation, and maintenance, the analysis provides meaningful hints for VIMS challenges in architecture, engineering, and facility management industries.
Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.
The mining methods are classified as the methods of data analysis and the knowledge acquisition and they are derived from the methods of “Knowledge Discovery”. Within the scope of these methods, there are two main variants associated with a form of data, i.e.: “data” and “text mining”. The author of the paper tries to find an answer to a question about helpfulness and usefulness of these methods for the purpose of knowledge acquisition in the construction industry. The very process of knowledge acquisition is essential in terms of the systems and tools operating based on knowledge. Nowadays, they are the basis for the tools which support the decision-making processes. The paper presents three cases studies. The mining methods have been applied to practical problems - the selection of an adhesive mortar coupled with alternative solutions, analysis of residential real estate locations under construction by a developer company as well as support of technical management of a building facility with a large floor area.
Decisions for building maintenance require integration of various types of information and knowledge created by different members of construction teams such as: maintenance records, work orders, causes and knock-on effects of failures, etc. Failing to capture and use this information/knowledge results in significant costs due to ineffective decisions. Majority of the current building maintenance systems mainly focus on capturing either information or knowledge. This research aims to develop an integrated system to capture information and knowledge of building maintenance operations when/after maintenance is carried out to understand how a building is deteriorating and to support preventive/corrective maintenance decisions. To develop the system, a number of case studies were investigated and interviews were conducted with professionals from different building maintenance departments in public organisations. This methodology helped identify the building maintenance process and the opportunities for knowledge capture and exchange. A taxonomy for building maintenance was then identified which enabled a formal approach for knowledge capture. The proposed system utilises the functions of information modelling techniques and knowledge systems to facilitate full retrieval of information and knowledge for maintenance work. The system consists of two modules; BIM module to capture relevant information and Case-Based Reasoning (CBR) module to capture knowledge. The system can help maintenance teams learn from previous experience and trace the full history of a building element and all affected elements by previous maintenance operations. It is concluded that the integrated knowledge-based BIM systems can provide advanced useful functions for construction operations. On the other hand, incorporating Knowledge Management principles embedded in CBR systems with Information Management principles embedded in BIM systems is a way forward for the transformation from ‘Building Information Modelling’ to ‘Building Knowledge Modelling’.
Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.
El original está disponible en www.journals.cambridge.org Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. Peer reviewed
The integration of operational information in building life-cycle management is a relevant topic within the facilities management domain. A previous study proposed a methodology based on Building Information Modelling (BIM) and Facility Management (FM) systems integration, underpinned by a Performance Information Model, to support the performance assessment and the maintenance management. The purpose of this paper is to extend and validate that methodology with an open standard approach. A subset of the Industry Foundation Classes (IFC) schema related to performance assessment and maintenance procedures is mapped into a relational database. The database supports the integration between BIM models and facilities information systems. Case studies including healthcare buildings test the database and a new application. The results contribute to the BIM adoption in the FM domain. Also, the studied and validated IFC schema subset can be considered as a contribution to the Model View Definition for FM.
In the existing building facilities context, the BIM substantial intervention concerning semantics and as-is information based portrayal for maintenance requirements is very confined. This confinement stresses the challenges associated with the lack and absence of reusing existing building facilities, as is domain knowledge. Within this scope, this paper proposes a novel approach for existing building facilities semantic-enrichment from indoor digital scanning portrayal phase to semantics depiction and management phase for renovation, rehabilitation, or restoration functions. In this paper, we present an ontological system, which relies on integrating the as-is information BIM and semantic web technology. The developed approach improves existing building facilities, as-is information formalization, and management for maintenance purposes. The paper outcome is a semantically integrated knowledge-based framework that can potentially: 1) constitute a semantic database for building facilities components as-is information using BIM; 2) overcome the lack and absence of retrieving and reusing building facilities as-is domain information; and (3) formalize the building facilities as-is knowledge in a computable way.
The complex, uncertain, and dynamic nature of the maintenance management environment is a source of concern to facility managers in hospitals due to the unexpected failure of building components, daily arrival of maintenance orders, and changes in schedule. In such circumstances, centralized systems become far-fetched because of their top-down approach which lacks a feedback mechanism and ignores new information. Therefore, to address any change, centralized systems have to be reformulated making it impractical, short-sighted, and problematic to adopt them in hospitals. As such, the use of centralized systems can lead to financial loss and dissatisfaction of patients. It, therefore, becomes necessary to establish a distributed maintenance management system to support the facility managers’ making-decision process. Multi-agent facility management system (MAFMS) was conceptually designed. This design employed Unified Modeling Language (UML) diagrams that illustrate the specific agents of the system and how these agents interact with each other. Maintenance data of a hospital building was used to initiate the multi-agent simulation for workflow management. The simulation results show the benefits of the proposed system, to reduce the response time to maintenance requests compared to the current maintenance system.
Building green retrofit is considered as an effective means of energy saving and achieving sustainable development goals. The success of a building retrofit is highly dependent on the retrofit strategies used. However, it remains challenging to select appropriate retrofit strategies for a specific retrofit building. There are numerous cases of building retrofit around the world, which can be collected, stored, and analysed. The knowledge gained from these cases could provide a useful reference for making decisions on new retrofitting projects. This study presents a case-based reasoning (CBR) approach to support building green retrofit decisions. A total of 71 retrofit cases in China were collected. The attributes of the retrofitting buildings were identified, including the general building information, component information, and energy and cost information. A synthetic optimisation weighting method was adopted based on both expert opinion and the attribute characteristics. A real retrofit case located in Shanghai was used as a case study to demonstrate the application of the CBR approach. The results indicate that the CBR approach can aid in identifying similar cases from the case database, and extracting valuable information from these. The experience and lessons learned from past cases can guide decision makers in making improved decisions on new green retrofit projects.
Construction automation has shown the potential to increase construction productivity after years of technical development and experimenting in its field. Exactly how, and the possible benefits and challenges of construction automation, though is unclear and missing from current research efforts. In order to better understand the comprehensive potential of construction automation for increasing construction productivity and the associated possible ramifications, an objective and data-driven review of the use of automation technologies in construction was done. The review was accomplished by using text mining methods on publically available written documents, covering a wide range of relevant data including scientific publications and social media. The text mining software VOS Viewer and RapidMiner Studio were used to determine the most promising areas of research through the analysis of scientific publications, and the main areas of concern of industry through the analysis of text on social media, respectively. These research areas and concerns are summarized in this paper, and based on them suggestions for industry are made to help advance the uptake of automation in construction.
Successful implementation of FM-enabled BIM can be achieved with 1) a clear definition of what FM-enabled BIM constitutes, 2) a seamless and practical process of collecting the FM-enabled BIM data throughout project development phases, and 3) a well-executed interoperability plan for exchanging data between BIM tools and facility management systems, such as Computerized Maintenance Management System (CMMS). This research contributes to the body of knowledge by first defining, and examining one of the first few pilots implementation of FM-enabled BIM, and discussing the challenges encountered and the lessons learned, and then by proposing a research framework for future researchers to systematically and strategically build the knowledge foundation on BIM for FM field. The implementation process described and the lessons learned captured in this pilot project provide valuable insights into the successful implementation of FM-enabled BIM.
Managerial decisions should be made by taking into account the priorities and objectives of different stakeholders' groups. Their preferences are usually expressed in words and are fuzzy concepts. This article analyses the peculiarities of companies’ work and decision - making within a fuzzy market situation. It also presents a developed fuzzy multi-criteria group decision-making model for practical problem solving by taking into account cost-effective management. This case study presents a selection of rational criteria set to use in the weighted cost-effectiveness analysis for facilities management strategies, in which integrated fuzzy multi-criteria decision-making methods are applied. The main findings are: the model is adopted to real- life; the main criteria groups are identified by a three-step Delphi technique; a rational strategy is determined and integrated in one model by the concept of Minkowski distance and fuzzy TOPSIS method, ARAS-F and fuzzy weighted product method. The proposed model is versatile and therefore can be applied for various problems were the experts’ knowledge needed for decision–making.
Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers.
Many document classification applications require human understanding of the reasons for data-driven classification decisions by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to millions of variables (words). Unfortunately, due to the high dimensionality, understanding the decisions made by document classifiers is very difficult. This paper begins by extending the most relevant prior theoretical model of explanations for intelligent systems to account for some missing elements. The main theoretical contribution is the definition of a new sort of explanation as a minimal set of words (terms, generally), such that removing all words within this set from the document changes the predicted class from the class of interest. We present an algorithm to find such explanations, as well as a framework to assess such an algorithm's performance. We demonstrate the value of the new approach with a case study from a real-world document classification task: classifying web pages as containing objectionable content, with the goal of allowing advertisers to choose not to have their ads appear on those pages. A second empirical demonstration on news-story topic classification shows the explanations to be concise and document-specific, and to be capable of providing understanding of the exact reasons for the classification decisions, of the workings of the classification models, and of the business application itself. We also illustrate how explaining the classifications of documents can help to improve data quality and model performance.
Since contractors' bidding behaviors are affected by numerous factors related both to the specific features of the project and dynamically changed situations, bidding decision problems are highly unstructured. No clear rules can be found in delivering a bidding decision. In this problem domain, decisions are commonly made based upon intuition and past experience. Case-based reasoning (CBR) is a subbranch of artificial intelligence. It solves new problems by matching against similar problems that have been encountered and resolved in the past. It is a useful tool in dealing with complex and unstructured problems, which are difficult if not impossible to be theoretically modeled. This paper presents a case-based reasoning bidding system that helps contractors with the dynamic information varying with the specific features of the job and the new situation. In this system, bid cases are represented by sets of attributes derived from a preliminary survey of several experienced bidders, focusing, respectively, on two reasoning subgoals: (1) Risk; and (2) competition. Through the system, similar cases can be retrieved to assess the possible level of competition and risk margin. A hypothetical example is explained and evaluated to demonstrate the feasibility of the method. The effectiveness of this system is tested by a Monte Carlo simulation in comparison to the conventional statistical method.
Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems.1 A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).
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