Formulating Procurement Selection Criteria through Case-Based Reasoning Approach

An appropriate procurement system is a catalyst to the success of a construction project. In practice, the solutions and outcomes of previous procurement selection decisions could be extremely useful in supporting decision making. As a technique that captures and reuses experiential knowledge, case-based reasoning (CBR) has a high potential for modeling the procurement selection decision within a complex dynamic environment. This paper examines the suitability of CBR approaches for procurement selection. The process involved in procurement selection is examined first. A conceptual framework for case-based procurement selection is proposed. The structure of a prototype model on procurement selection criteria (PSC) formulation is presented in this paper. The model applies the CBR approach to procurement criteria selection irrespective of the variability in the characteristics of the client, project, and external environment.     

CB-Contract: Case-Based Reasoning Approach to Construction Contract Strategy Formulation

The formulation of an appropriate contract strategy is a fundamental yet critical aspect of contractual arrangement for a project. This paper presents a decision support system (DSS) prototype for contract strategy formulation using the case-based reasoning (CBR) approach. The prototype is called CB-Contract. An overview on the scope and formulation process of contract strategy is provided. Contract strategy is regarded as comprising four main substrategies: (1) work packaging; (2) functional grouping; (3) contract type; and (4) award method. The significance of CBR in contract strategy formulation is examined. The overall architecture of CB-Contract is discussed, with particular emphasis on its knowledge component. A case structure for case representation has been suggested taking into account the important factors that need to be considered while formulating a contract strategy, in particular project characteristics, client’s objectives, and client’s comparative advantages. Issues pertaining to case indexing and similarity calculation for efficient retrieval of similar cases are explored. An illustrative example to obtain similar cases for a given set of project information is presented. Case adaptation that considers the robustness of each substrategy, the compatibility of the substrategies, and the expected effectiveness of the contract strategy is also included in the illustrative example.     

Case-Based Reasoning Approach to Construction Safety Hazard Identification: Adaptation and Utilization

Risk assessment, consisting of hazard identification and risk analysis, is an important process that can prevent costly incidents. However, due to operational pressures and lack of construction experience, risk assessments are frequently poorly conducted. In order to improve the quality of risk assessments in the construction industry, it is important to explore the use of artificial intelligence methods to ensure that the process is efficient and at the same time thorough. This paper describes the adaptation process of a case-based reasoning (CBR) approach for construction safety hazard identification. The CBR approach aims to utilize past knowledge in the form of past hazard identification and incident cases to improve the efficiency and quality of new hazard identification. The overall approach and retrieval mechanism are described in earlier papers. This paper is focused on the adaptation process for hazard identification. Using the proposed CBR approach, for a new work scenario (the input case), a most relevant hazard identification tree and a set of incident cases will be retrieved to facilitate hazard identification. However, not all information contained in these cases are relevant. Thus, less relevant information has to be pruned off and all the retrieved information has to be integrated into a hazard identification tree. The proposed adaptation is conducted in three steps: (1) pruning of the retrieved hazard identification tree; (2) pruning of the incident cases; and (3) insertion of incident cases into the hazard identification tree. The adaptation process is based on the calculation of similarity scores of indexes. A case study based on actual hazard identifications and incident cases is used to validate the feasibility of the proposed adaptation techniques.     

Key Factors in Bid Reasoning Model

The bidding decision is a complex problem affected by numerous factors. The current study collected a list of determining factors from the results of past research and opinions of six experienced practitioners in competitive bidding. Based on these factors, a bid reasoning model was established to go deeply into the bid decision process. Differing from other earlier work, this model-oriented study focuses on the effects of the determining factors on four reasoning subgoals: competition, risk, company's position in bidding, and need for work. Their different contributions to each reasoning subgoal were reviewed under three main construction procurement methods, namely, unit rate contract, lump sum contract, and design∕build contract. The Analytic Hierarchy Process technique was applied in this study. A survey questionnaire was developed with four hierarchies being formulated respectively for the four reasoning subgoals. The survey was conducted in two steps, the pilot survey among the six experienced practitioners and the subsequent one among 153 top contractors in Singapore. From this survey, a set of top key factors and their relative importance weights were identified. This result together with the bid reasoning model can serve as the framework for the further development of the bid decision support system.     

REILP Approach for Uncertainty-Based Decision Making in Civil Engineering

The civil and environmental decision-making processes are plagued with uncertain, vague, and incomplete information. Interval linear programming (ILP) is a widely applied mathematical programming method in assisting civil and environmental decision making under uncertainty. However, the existing ILP decision approach is found to be ineffective in generating operational schemes for practical decision support due to a lack of linkage between decision risk and system return. In addition, the interpretation of the ILP solutions represented as the lower and upper bounds of decision variables can cause problems of infeasibility and nonoptimality in the resulted implementation schemes. This study proposed a risk explicit ILP (REILP) approach to overcome the limitations of existing ILP approaches. The REILP explicitly reflects the tradeoffs between risk and system return for a decision-making problem under an interval-type uncertainty environment. A risk function was defined to enable finding solutions which maximize system return while minimizing system risk, hence leading to crisp solutions that are feasible and optimal for practical decision making. A numerical experiment on land-use decision making under total maximum daily load was conducted to illustrate the REILP approach. The model results show that the REILP approach is able to efficiently explore the interval uncertainty space and generate an optimal decision front that directly reflects the tradeoff between decision risks and system return, allowing decision makers to make effective decision based on the risk-reward information generated by the REILP modeling analysis.     

Comparison of Case-Based Reasoning and Artificial Neural Networks

The outcome of construction litigation depends on a large number of factors. To predict the outcome of such litigation is difficult because of the complex interrelationships between these many factors. Two attempts are reported in the literature that use, respectively, case-based reasoning (CBR) and artificial neural networks (ANN) to overcome this difficulty. These studies were conducted by using the same 102 Illinois circuit court cases; an additional 12 cases were used for testing. Prediction rates of 83% in the CBR study and 67% in the ANN study were obtained. In this paper, CBR and ANN are compared, and their advantages and disadvantages are discussed in light of these two studies. It appears that CBR is more flexible when the system is updated with new cases, has better explanation facilities, and handles missing data and a large number of features better than ANN in this domain. If the use of CBR and ANN is understood better and if, as a result, the outcome of construction litigation can be predicted with reasonable accuracy and reliability, all parties involved in the construction process could save considerable money and time.     

Military Facility Cost Estimation System Using Case-Based Reasoning in Korea

Numerous cost estimations are made repetitively in the initial stages of construction projects in response to ongoing scope changes and often need to be recalculated frequently. In practice, the square foot method, considered an effective method for time-saving, is widely used. However, this method requires a great amount of effort to calculate a unit price and does not consider the uniqueness of each case. Thus, the use of the square foot method could bring about unwanted consequences. For example, in the case of military projects in Korea, significant differences have been reported between estimations made using this method and the actual costs. In an effort to deal with this challenging issue, this research develops a military facility cost estimation (MilFaCE) system, based on case-based reasoning (CBR), using case data from 422 construction projects at 16 military facilities. Based on system validation experiments involving 10 military officers (engineers), the effectiveness of the system in terms of estimation accuracy and user-friendliness is confirmed. Consequently, this research can be a CBR application example of construction cost estimation and a basis for further research into the development of cost estimate systems.     

Construction Planning Method Using Case-Based Reasoning (CONPLA-CBR)

During the construction process, there occur many unexpected events that hinder timely completion of a project. One plausible solution in formulating a robust plan against such uncertainties is to provide the proper construction duration by utilizing as-built schedules in which past events are stored for similar future projects. Many schedulers thus develop schedules based on similar past schedules, taking into consideration the dynamic construction environment. As a result, construction schedulers normally refer to similar past schedules for their current projects. Few studies on the reuse of past schedules using case-based reasoning (CBR) have been conducted, and those that are available are limited to specific areas of construction such as apartment-building construction and boiler manufacturing. This research has an emphasis on developing a CBR-based general planning tool with higher applicability, which consists of generic attributes with the capability to be customized to the given project. To address this issue, construction planning by CBR (CONPLA-CBR) is presented as a generic planning tool for various types of construction projects. CONPLA-CBR, with the dynamic case approach and construction schedule data mart, also developed in this research, helps schedulers to utilize past schedules. CONPLA-CBR was not only verified to be of practical utility by experts, but also, because it uses past cases to which the successor relationships pertain, it does not require users to input all relationships. Whereas the proposed CONPLA-CBR generates master schedules at the preconstruction stage, its concept can also be applied to the construction stages to generate more detailed, for example, weekly or monthly, schedules. Thereby, CONPLA-CBR would enhance construction performance through the increased application of CBR in construction.     

Bid Compensation Decision Model for Projects with Costly Bid Preparation

For projects with high bid preparation cost, it is often suggested that the owner should consider paying bid compensation to the most highly ranked unsuccessful bidders to stimulate extra effort or inputs in bid preparation. Whereas the underlying idea of using bid compensation is intuitively sound, there is no theoretical basis or empirical evidence for such suggestion. Because costly bid preparation often implies a larger project scale, the issue of bid compensation strategy is important to practitioners and an interest of study. This paper aims to study the impacts of bid compensation and to develop appropriate bid compensation strategies. Game theory is applied to analyze the behavioral dynamics between competing bidders and project owners. A bid compensation model based on game theoretic analysis is developed in this study. The model provides equilibrium solutions under bid compensation, quantitative formula, and qualitative implications for the formation of bid compensation strategies.     

Preliminary Cost Estimation Model Using Case-Based Reasoning and Genetic Algorithms

This study proposes a preliminary cost estimation model using case-based reasoning (CBR) and genetic algorithm (GA). In measuring similarity and retrieving similar cases from a case base for minimum prediction error, it is a key process in determining the factors with the greatest weight among the attributes of cases in the case base. Previous approaches using experience, gradient search, fuzzy numbers, and analytic hierarchy process are limited in their provision of optimal solutions. This study therefore investigates a GA for weight generation and applies it to real project data. When compared to a conventional construction cost estimation model, the accuracy of the CBR- and GA-based construction cost estimation model was verified. It is expected that a more reliable construction cost estimation model could be designed in the early stages by using a weight estimation technique in the development of a construction cost estimation model.     

Case-Based Reasoning for Construction Hazard Identification: Case Representation and Retrieval

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach.     

Case-Based Reasoning for Converting Working Stress Design-Based Bridge Ratings to Load Factor Design-Based Ratings

In 1995, the Federal Highway Administration (FHWA) required that all bridges, regardless of the design method used for the original design, be based on the load factor design (LFD) method. In order to comply with the FHWA requirements, state departments of transportation have converted to the LFD method for all new bridge ratings. Further, all existing bridges previously rated using the working stress design (WSD) method must be converted to the LFD method. Consequently, thousands of bridges must be rerated using the LFD method. Steel bridges rated by the WSD method have critical data missing to make the proper conversion to the LFD method. This paper presents a methodology and an intelligent decision support system to help bridge engineers convert a WSD-based bridge rating to the LFD-based rating with little human effort using the artificial intelligence approach of case-base reasoning. The proposed methodology can help bridge engineers create the missing LFD-based data efficiently and quickly with a minimum amount of work. This research demonstrates how bridge engineers can use a novel computing paradigm and modern computer tool to convert an antiquated database to current design.     

Two Pairs of Conflicting Motives in Decision Making

A concept is proposed that decision making consists of resolving two conflicts. One of them is between the desire to make an accurate decision and the desire to minimize the effort of making a choice. The second conflict is between the desire to make an accurate decision and the desire that this decision is distinct. To operationalize basic motives involved in decision making, as assumed in the model, a new process tracing technique has been developed. Using this technique, three experiments were performed to test the predictions of the model. The experiments supported the predictions concerning the conflict between accuracy and distinctness. Similarity of choice alternative and decision importance appeared to affect the way the conflict between accuracy and distinctiveness is solved. An alternative explanation of the results obtained and perspectives of further research are discussed. Copyright 1998 Academic Press.     

Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction

This paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning (CBR) prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.     

On the Role of Recognition in Decision Making.

In 2 experiments, the authors sought to distinguish between the claim that recognition of an object is treated simply as a cue among others for the purposes of decision making in a cue-learning task from the claim that recognition is attributed a special status with fundamental, noncompensatory properties. Results of both experiments supported the former interpretation. When recognition had a high predictive validity, it was relied on (solely) by the majority of participants; however, when other cues in the environment had higher validity, recognition was ignored, and these other cues were used. The results provide insight into when, where, and why recognition is used in decision making and also question the elevated status assigned to recognition in some frameworks (e.g., D. G. Goldstein & G. Gigerenzer, 2002). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparative Evaluation of Analytical and Intuitive Decision Making

This paper examines a technique for evaluating the differences in value among rank-ordered alternatives using both analytical and intuitive decision making. This technique was applied to the ranking of alternative disinfection methods for both a water and wastewater plant. The paper further provides a framework for analyzing and reconciling the differences between analytical and intuitive decision models. Neither exists in a strictly mathematical closed form, so it is important to investigate both to understand where the differences in alternative recommendations reside. A framework such as the one presented here is key to unlocking the subjectivity inherent in and the difficulty surrounding value structure articulation.     

Social Motives and Strategic Misrepresentation in Social Decision Making.

In 4 experiments, the authors studied the influence of social motives on deception and strategic misrepresentation. In a newly developed information provision game, individuals faced a decision maker whose decision would affect both own and other's outcomes. By withholding information or by giving (in)accurate information about payoffs, participants could try to influence other's decision making. Less accurate and more inaccurate information was given when the decision maker was competitive rather than cooperative (Experiment 1), especially when participants had a prosocial rather than selfish value orientation (Experiments 3 and 4). Accurate information was withheld because of fear of exploitation and greed, and inaccurate information was given because of greed (Experiment 2). Finally, participants engaged in strategic misrepresentation that may trick competitive others into damaging their own and increasing the participant's outcomes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

多属性决策理论在矿山开采规模中的应用

矿产资源的合理开发与利用直接关系到矿山今后的兴衰,而生产规模又直接影响到矿山建设和生产及其经济效益,因此,矿山开采规模方案的选择对矿山企业来说有着重要而深远的意义.以江西某铜矿为研究对象,结合矿山生产现状,提出了三种生产规模方案,利用理想点法选出了样本矿山1500 t/d生产规模下的最佳开采方案.     

Reasoning counterfactually: Making inferences about things that didn't happen.

The authors investigated the relationship between reasoners' understanding of subjunctive conditionals (e.g., if p had happened, then q would have happened) and the inferences they were prepared to endorse. Reasoners who made a counterfactual interpretation of subjunctive statements (i.e., they judged the statement to imply that p and q did not happen) endorsed different inferences than those who did not. Those who made a counterfactual interpretation were more likely to (a)judge the situation in which p and q occurred to be inconsistent with the conditional statement and (b) make negative inferences such as modus tollens (i.e., – q therefore – p ). These findings occurred with familiar and unfamiliar content, affirmative and negative conditionals, and conditional and biconditional relations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Group Decision Making and Perceived Decision Success: The Role of Communication Medium.

The authors investigated the role of communication medium on the relationship between team member perceptions and decision success. Seventy-three 3-person groups participated in a consensus intellective task either face-to-face (FTF) or via computer-mediated communication (CMC). The participants also assessed their group's decision success and team member competencies. CMC group members' success perceptions significantly predicted their group's performance, but FTF group members' perceptions did not. Furthermore, only CMC group members' judgments regarding their group's problem-solving ability significantly predicted their decision success. Last, judgments of decision success mediated the relationship between perceptions of members' problem-solving ability and decision success only for CMC group members. Implications are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)