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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: This paper tries to analyze common bridge construction defects, classify them into appropriate groups, and redefine them as a precautionary measure and means to improve quality in bridge construction. For this purpose, data on bridge construction since January 2007 were obtained from the evaluation report of the Public Construction Committee (PCC) of Taiwan. Bridge construction defects were classified according to their characteristics. A constraint-based clustering method and affinity diagram (KJ method) are proposed and used. This method can simultaneously treat mixed data types; moreover, it can incorporate user-specified constraints. The quality or safety issues, the unit-in-charge (Government authorities/project owners/contractor), and the properties of the defects (construction/audit/documents/others) are the sorting attributes. The constraint is avoiding empty clusters or clusters having very few objects. The results revealed five major defect classifications: safety and environment, construction site defects, supervision/control process, construction quality documents, and others. [Copyright &y& Elsevier]

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]

Abstract: Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model for microtunneling decision support system (MDS) that can be used as a basis for developing more effective microtunneling design and construction. The model objectives are to: (1) develop a description of local geology that reflects the uncertainty of the information on which it is based and (2) provide the input data necessary for other decision support systems. MDS is composed of two main modules: (1) geology prediction model (GPM) module which is based on Neural-Autoregressive Hidden Markov Model and (2) excavation method selection module to select appropriate excavation method based on GPM result. In order to validate the proposed model, a microtunneling project: Zhong-he drainage water tunnel in Taiwan, was used as a case study. The result shows that the MDS model achieves these objectives to a satisfactory degrese. [ABSTRACT FROM AUTHOR]

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]

The earned value management (EVM) method is considered as a powerful tool that supports the management of project scope, time, and cost. However, conventional EVM may cause some project managers to ignore the information that it can provide about the performance variations of their projects as well as their causes and effects. This paper attempts to refine and improve the performance of traditional EVM by the introduction of statistical control chart techniques. Individual control charts are used as tools to monitor project performance data so that adverse changes can be detected in a timely manner. This allows analysis of in-progress project cost and schedule performance variations and highlights possible needs for corrective action. A comparative analysis between traditional EVM and the new approach is undertaken to identify the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Abstract: When a number of bridges are to be maintained yearly, contract engineers need to package certain bridges based upon the specified conditions. Traditional contract packaging is performed subjectively and manually. The purpose of this paper is to develop an objective and automatic decision-making process for contract packaging. Contract Packaging Technique (CPT), which is derived from k-prototypes, can determine appropriate contract packages by considering the inherent conditions of the infrastructure to be maintained. It can simultaneously handle user-specified constraints and mixed data types such as bridge locations, bridge types, bridge construction materials and maintenance costs. Validation was performed in this study to illustrate that the CPT can effectively divide a huge bridge maintenance plan into several appropriate contract units. [Copyright &y& Elsevier]

Abstract: The New Austrian Tunneling Method (NATM) is the most popular construction method in rock tunnel projects. Geological logging and rock mass classification are two major tasks prior to NATM tunnel excavation. Excavation face conditions are presently determined and logged by manual visual investigation for most, if not all tunnel projects. 2D geotechnical maps may then be drawn to show the locations and directions of weak planes. It is a costly and time-consuming process. To overcome the limitations of conventional approaches, image processing and information management techniques were adopted for tunnel face image storage, management, processing, interpolation, reconstruction, and visualization. An information system was used to manage and manipulate tunnel face related information, including geological description, rock mass rating and excavation face images. Image processing techniques in both spatial and frequency domains were applied to analyze and identify significant geological features (such as faults, joints and shear zones) from excavation faces. Shape-based image interpolation was then adopted to interpolate inter-slices between two neighboring captured images. Finally, a 3D image reconstruction and visualization environment was established to effectively assist geologists and tunnel project engineers in analyzing and evaluating geological characteristics of tunnel excavation faces. [Copyright &y& Elsevier]

Precast production tasks mainly consist of reinforcement cage prefabrication, form fabrication, installment of reinforcement cages, mosaic or tiling fitting, placing concrete, steam curing, cleaning and finishing operations. These tasks are generally arranged as a flow shop process, in which all the precast elements are processed in the same processing order through all the tasks. The precast production needs to take into account the impact of limited resources, including equipment and skilled labor, on production makespan. Moreover, to economically and effectively utilize these valuable resources, mixed production gains a potential advantage over the conventional separate production under resource constraints. A resource-constrained mixed-production flow-shop scheduling system is proposed in this paper. A genetic algorithm-based searching technique is adopted in the system to provide the optimal or near-optimal combination of production sequences, resource utilization, and minimum makespan in consideration of resource constraints and mixed production. Sensitivity analysis is further conducted to identify the impact of mixed production strategies on the overall precast production makespan. [Copyright &y& Elsevier]

To meet the physical limits of construction resources, to avoid day-to-day fluctuation in resource demands, and to maintain an even flow of application for construction resources, resource leveling is needed in the construction industry. Traditional resource leveling models assume activity durations to be deterministic. Nevertheless, activity duration may be uncertain, owing to variations in the overall environment, such as weather, site congestion, and productivity level. A new optimal construction resource leveling model is proposed in this paper, in which the combinative effects of both uncertain activity duration and resource leveling are taken into consideration. Monte Carlo simulation is used to model the uncertainties of activity duration. A searching technique using genetic algorithms (GAs) is then adopted to search for the impact of uncertain activity durations on the probabilistic optimal resource leveling indices. The model can effectively provide probabilistic optimal resource leveling indices for multiple construction resources subjected to the objective of resource leveling, and the impact of influence factors on the probabilistic resource-leveling scheduling problems.Key words: resource leveling, genetic algorithms, simulation, probabilistic scheduling.Afin de gérer les limites physiques des ressources en construction, d'éviter les fluctuations quotidiennes des demandes de ressources, et de maintenir un niveau d'applications constant pour les ressources en construction, la stabilisation des ressources est requise dans l'industrie de la construction. Les modèles traditionnels de stabilisation des ressources supposent que la durée des activités est de nature déterministe. Néanmoins, la durée des activités peut être sujette à des incertitudes dues à des variations de l'environnement en géneral, telles que les conditions météorologiques, la congestion sur le site, le niveau de productivité, etc. Cet article propose un nouveau modèle optimal de stabilisation des ressources en construction, dans lequel les effets combinés des incertitudes dans la durée des activités et la stabilisation des ressources sont pris en considération. La simulation de Monte Carlo est employée afin de modéliser les incertitudes dans la durée des activités. Une technique de recherche faisant usage des algorithmes génétiques (« genetic algorithms : GAs ») est ensuite utilisée afin de déterminer l'impact des incertitudes dans la durée des activités sur les indices probabilistes optimaux de stabilisation des ressources. Le modèle peut efficacement produire des indices probabilistes optimaux de stabilisation des ressources, et ce pour des ressources en construction multiples sujettes à l'objectif de stabilisation des ressources et à l'impact de facteurs d'influence sur les problèmes de planification probabiliste de stabilisation des ressources.Mots clés : stabilisation des ressources, algorithmes génétiques, simulation, planification probabiliste.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

This article adopted the concept of decision support to establish a CPM-based construction quality inspection and decision-aid system (CQIDS) to improve ineffective field quality inspection processes. The system consists of two main subsystems: the database subsystem and the decision subsystem. The database subsystem contains information about quality specifications, shop drawings, checklists, and corrective actions that comprises a multimedia network. The decision subsystem contains statistical algorithms to facilitate in situ quality data analysis. Moreover, in order to execute quality management in a timely fashion, quality management timetables are generated automatically from the CQIDS and sent to field quality supervisors by integrating quality management activities within the project's own CPM network. [ABSTRACT FROM AUTHOR]

Presents information on a study which proposed an optimal resource-constrained construction scheduling model in analyzing effects of activity duration. Methods on construction planning and scheduling; Details of fuzzy set theory; Conclusions.

After the long-term operation of reservoir facilities, they will become nonoperational due to the material deterioration and the performance degradation. One of crucial decisions is to determine the maintenance or replacement of the facilities in a cost-effective manner. Conventional replacement models seldom consider the maintenance effect. The facilities after maintenance are generally not as good as new, but are relatively restored. The target of this study is to establish a replacement decision model of the reservoir facilities under imperfect maintenance. By combining the theories of reliability analysis, imperfect maintenance, and engineering economics, the best timing of replacement that achieves cost-effectiveness is analyzed and proposed. Lastly, based on the design of experiments (DOE) and simulation, the regression curve chart for the economical replacement decision is established. Once the failure rate, the age of recovery after maintenance, and the ratio of maintenance cost to replacement cost are estimated based on historical data, the cost-effective replacement time of hydraulic machinery facilities will be efficiently determined. [ABSTRACT FROM AUTHOR]