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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]

A fall (also referred to as a tumble) is the most common type of accident at steel construction (SC) sites. To reduce the risk of falls, current site safety management relies mainly on checklist evaluations. However, current on-site inspection is conducted under passive supervision, which fails to provide early warning to occupational accidents. To overcome the limitations of the traditional approach, this paper presents the development of a fall risk assessment model for SC projects by establishing a Bayesian network (BN) based on fault tree (FT) transformation. The model can enhance site safety management through an improved understanding of the probability of fall risks obtained from the analysis of the causes of falls and their relationships in the BN. In practice, based on the analysis of fall risks and safety factors, proper preventive safety management strategies can be established to reduce the occurrences of fall accidents at SC sites. [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]

Fall or tumble is one of the most common accidents in bridge construction. Failing to implement safety management and training effectively may result in serious occupational accidents. Current site safety management relies mostly on checklist evaluation; however, its effectiveness is limited by the experience and the abilities of the evaluators, which may not consistently achieve the goal of thorough assessment. Recently, several systematic safety risk assessment approaches, such as Fault Tree Analysis (FTA) and Failure Mode and Effect Criticality Analysis (FMECA), have been used to evaluate safety risks at bridge projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. In order to overcome the limitations of the traditional approach, in this paper a fall risk assessment model for bridge construction projects is developed by establishing a Bayesian network (BN) based on Fault Tree (FT) transformation. The model was found to provide much better site safety management ability by enabling better understanding of the probability of fall risks through the analysis of fall causes and their relationships in a BN. The system has been used to analyze and verify safety practices at five cantilever bridge construction projects currently under construction in Taiwan. It was found that BN analysis is consistent with the conventional safety performance assessment. In practice, based upon the BN analysis by inputting prior information about the site safety management, the probabilities of fall risks and their sensitive factors can be effectively assessed. Proper preventive safety management strategies could then be established to reduce the occurrences of fall accidents at the bridge construction projects. [ABSTRACT FROM AUTHOR]

Abstract: There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. [Copyright &y& Elsevier]

Abstract: This paper considers a difficult but practical circumstance of civil infrastructure management—deterioration/failure data of the infrastructure system are absent while only condition-state data of its components are available. The goal is to develop a framework for estimating time-varying reliabilities of civil infrastructure facilities under such a circumstance. A novel method of analyzing time-varying condition-state data that only reports operational/non-operational status of the components is proposed to update the reliabilities of civil infrastructure facilities. The proposed method assumes that the degradation arrivals can be modeled as a Poisson process with unknown time-varying arrival rate and damage impact and that the target system can be represented as a fault-tree model. To accommodate large uncertainties, a Bayesian algorithm is proposed, and the reliability of the infrastructure system can be quickly updated based on the condition-state data. Use of the new method is demonstrated with a real-world example of hydraulic spillway gate system. [Copyright &y& Elsevier]