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Price negotiation in construction procurement is a form of decision making where contractor and supplier jointly search for a mutually agreed solution. In price negotiation, with information available about the agent's preferences, a negotiation may result in a mutually beneficial agreement. However, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a beneficial agreement. In order to overcome this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent's preference in bilateral negotiation. The proposed approach employs Bayesian theory to analyze the opponent's historical offers and to approximately predict the opponent's preference over negotiation issue. A Nash equilibrium algorithm is also integrated into the prediction approach to help agents on how to propose beneficial offers based on the prediction results. Validation results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency. [ABSTRACT FROM AUTHOR]

Construction procurement is a key business where price negotiation is commonly required to reach final contractual agreement. However, even simple negotiations often result in infeasible agreements. The uncertain and limited supplier information as well as complex correlations among various factors affecting supplier behaviors make the contractor difficult to decide the appropriate offer price (OP) and vice versa. This study proposes a novel Fuzzy Bayesian Game Model (FBGM) for improving the prediction effectiveness of negotiation behaviors. The performance of the proposed FBGM was evaluated in the case where an agent uses the counter-OP of an opponent to learn the negotiation strategy of the opponent. The validation analysis shows that the sequential updating process of FBGM significantly improves the estimation ability of negotiators. The proposed model also gives a comprehensive view of negotiation scenarios by considering all possible negotiation cases. Using FBGM, negotiators can apply flexible strategies to optimize their own profit with a reasonable negotiation time. [ABSTRACT FROM AUTHOR]

Abstract: Uncertain ground conditions represent the primary source of risk in underground tunnel construction. However, this problem can be solved by developing an accurate, probabilistic description of the geology. This paper presents a general model for probability based determination of tunnel geology that can be used as a basis for developing more effective decision support systems for tunneling design and construction. The proposed model is based on a Hidden Markov Model (HMM) and a neural network (NN). An approximate inference technique – a Particle Filter (PF) Algorithm – is used to simulate the geological parameters. This model overcomes the deficiencies of existing models by readily incorporating all available geologic information and updating geologic predictions based on observations given by the neural network. In order to validate the proposed model, the “Drainage Water Tunnel Project” at Zhong-He, Taipei, Taiwan was used. The results showed that the Neural-HMM model provides high accuracy in geological prediction. [Copyright &y& Elsevier]

Both mixed data types and cluster constraints are frequently encountered in the classification problems of construction management. For example, in a bridge let project, engineers generally group the bridges into several subgroups based on their proximities, structure type, material, etc. Moreover, constraints may be set for each cluster to ensure the project’s overall effectiveness. In this study, an effective clustering algorithm – the constrained k-prototypes (CKP) algorithm – is proposed to resolve the abovementioned problems. Several tests and experimental results have shown that CKP cannot only handle mixed data types but also satisfy user-specified constraints. In order to demonstrate the applicability of CKP, it is also applied to real-world problems in construction management. [Copyright &y& Elsevier]