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Purpose: This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions. Design/methodology/approach: A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach. Findings: The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects. Originality/value: This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures. [ABSTRACT FROM AUTHOR]

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: Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000–2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents. [Copyright &y& Elsevier]

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]

Abstract: The purpose of this present study is to investigate the characteristic factors responsible for occupational accident occurrence for small construction enterprises in Taiwan, using methodologies of descriptive statistics, correlation coefficient analysis as well as ANOVA. The data used in this study was collected from the Council of Labor Affairs of Taiwan Central Government between years 2000 and 2007. From the results of this study, it was found that the factors influencing occupational accident development for small construction enterprises included the health and safety management skills, employers’ values on the health and safety practices, and the degree of compliance with labor safety laws and regulations. The results also indicated that occupational accidents tend to occur (1) during the worker’s first day at the workplace, (2) when the construction project has an excessively low health and safety management, (3) when employer did not provide personal protection equipments to the workers, (4) when personal protection equipment was not correctly used, and (5) when workers failed to adopt safeguards or ignored hazard warning signs in the workplace. To reduce the overall occurring rate of occupational accidents per 1000 persons in the construction industry, it is important to implement required health and safety practices and training effectively to ensure that all workers acknowledge and follow these requirements regulations when working. [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]

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]

Abstract: Most models of the incidence of occupational accidents in the construction industry are composed of multiple factors. Although statistical techniques can be used to infer cause-and-effect relationships among these factors, the large number of factors involved and the complexity of the relationships among them make it difficult for managers to identify potential hazards in construction projects and thus develop effective safety procedures. This study addresses this problem by using the association-rule method of data mining in performing an analysis of 1347 accidents in the Taiwan construction industry during the period 2000–2007. The association-rule method enables potential cause-and-effect relationships to be identified among the many factors that play a role in occupational accidents in the construction industry. The study finds that such accidents tend to occur when certain combinations of hazards are present – especially working in high places without protective measures, loss of balance when in motion, failure to use protective equipment, insufficient experience, and injurious contact with unstable structures. These hazards are especially evident in small enterprises with less than 10 persons. The results can help management to formulate effective safety policies with regard to management shortcomings and staff training. [Copyright &y& Elsevier]