Postprocessing the Hybrid Method for Addressing Uncertainty in Risk Assessments

In a previous paper in this Journal, a “hybrid method” was proposed for the joint propagation of probability distributions (expressing variability) and possibility distributions (i.e., fuzzy numbers, expressing imprecision or partial ignorance) in the computation of risk. In order to compare the results of the hybrid computation (a random fuzzy set) to a tolerance threshold (a tolerable level of risk), a postprocessing method was proposed. Recent work has highlighted a shortcoming of this postprocessing step which yields overly conservative results. A postprocessing method based on Shafer’s theory of evidence provides a rigorous answer to the problem of comparing a random fuzzy set with a threshold. The principles behind the new postprocessing scheme are presented and illustrated with a synthetic example.     

Risk Assessment Methodology for Underground Construction Projects

This paper presents a risk assessment methodology for underground construction projects. A formalized procedure and associated tools were developed to assess and manage the risks involved in underground construction. The suggested risk assessment procedure is composed of four steps of identifying, analyzing, evaluating, and managing the risks inherent in construction projects. The main tool of the proposed risk assessment methodology is the risk analysis software. The risk analysis software is built upon an uncertainty model based on fuzzy concept. The fuzzy-based uncertainty model is designed to consider the uncertainty range that represents the degree of uncertainties involved in both (1) probabilistic parameter estimates and (2) subjective judgments. Other tools developed in this study include the survey sheets for collecting risk-related information and the detail check sheets for risk identification and analysis. The paper finally discusses a detailed case study of the developed risk assessment methodology performed for a subway construction project in Korea.     

Comparing Two Methods for Addressing Uncertainty in Risk Assessments

The Monte Carlo method is a popular method for incorporating uncertainty relative to parameter values in risk assessment modeling. But risk assessment models are often used as screening tools in situations where information is typically sparse and imprecise. In this case, it is questionable whether true probabilities can be assigned to parameter estimates, or whether these estimates should be considered as simply possible. This paper examines the possibilistic approach of accounting for parameter value uncertainty, and provides a comparison with the Monte Carlo probabilistic approach. The comparison illustrates the conservative nature of the possibilistic approach, which considers all possible combinations of parameter values, but does not transmit (through multiplication) the uncertainty of the parameter values onto that of the calculated result. In the Monte Carlo calculation, on the other hand, scenarios that combine low probability parameter values have all the less chance of being randomly selected. If probabilities are arbitrarily assigned to parameter estimates, without being substantiated by site-specific field data, possible combinations of parameter values (scenarios) will be eliminated from the analysis as a result of Monte Carlo averaging. This could have a detrimental impact in an environmental context, when the mere possibility that a scenario may occur can be an important element in the decision-making process.     

Stochastic Finite-Element Approach to Quantify and Reduce Uncertainty in Pollutant Transport Modeling

One of the important issues in simulation of contaminant transport in the subsurface is how to quantify the hydraulic properties of soil that are randomly variable in space because of soil heterogeneity. Stochastic approaches have the potential to represent spatially variable parameters, making them an appropriate tool to incorporate the effects of the spatial variability of soil hydraulic properties on contaminant fate. This paper presents development and application of a numerical model for simulation of advective and diffusive-dispersive contaminant transport using a stochastic finite-element approach. Employing the stochastic finite-element method proposed in this study, the response variability is reproduced with a high accuracy. Comparison of the results of the proposed method with the results obtained using the Monte?Carlo approach yields a pronounced reduction in the computation cost while resulting in virtually the same response variability as the Monte?Carlo technique.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Hybrid Soft Computing Approach for Mining of Complex Construction Databases

The paper presents a hybrid soft computing system for mining of complex construction databases. The proposed approach hybridizes soft computing techniques, such as fuzzy logic, artificial neural networks (ANNs), and messy genetic algorithms (mGAs), to form a novel computational method for mining of human understandable knowledge from historical databases. The hybridization combines the merits of explicit knowledge representation of fuzzy logic decision-making systems, learning abilities of ANNs, and global search of mGAs. A hybrid soft computing system (HSCS) is developed for mining complex databases in construction with three characteristics: scarcity, incompleteness, and uncertainty. Real-world construction data repositories are selected to test the capabilities of the proposed HSCS for data-mining under the above-mentioned complex conditions. The testing results show the promising potential of the proposed HSCS for mining of complex databases in construction.     

Determination of the total concentration and speciation of Al(III) in tea infusions

The aluminium species in different tea infusions were investigated, by determining their stability constants and concentration. This was done for some particular samples using a simple experimental method based on the sorption of aluminium on the strongly sorbing resin Chelex 100, by a batch procedure. From the thermodynamic information obtained it is possible to calculate the concentration of the different species, and in particular that of the free metal ion, which is very important for evaluating the adsorption of aluminium on biological membranes. It was found that aluminium in the tea infusions here considered is present at high total concentration, approximately 0.1 mM, but mainly linked to strong complexes, for instance with side reaction coefficient higher than 10(5.11) at pH 3.95 in one case (tea 1). This could be the reason for the low toxicity of aluminium in tea. These strong complexes were not dissociated even in the presence of Chelex 100. In this case only a limiting value of the reaction coefficient could be evaluated. The presence of the very strong complexes was found in all the tea sample here considered. In two of the considered samples (one black and one green tea) a part of Al(III) was linked to less strong complexes, for example with a reaction coefficient 10(4.14) (tea 2, pH 4.20). The presence in the considered tea infusions of other substances able to complex aluminium was also detected, by the well known ligand titration procedure, at concentration ranging from 0.65 to 3.37 mM in three tea infusions, and at somewhat higher concentration in the case of the ready drink, which was also considered for comparison.     

Fuzzy Parametric Programming Model for Integrated Solid Waste Management under Uncertainty

Solid waste management (SWM) is increasingly becoming a challenging task for the municipal authorities due to increasing waste quantities, changing waste composition, decreasing land availability for waste disposal sites, and increasing awareness about the associated environmental risk. This paper presents a fuzzy parametric programming model for the selection of the treatment and disposal facilities and optimum capacity planning and waste allocation under uncertainty associated with the long-term planning for SWM. The model dynamically locates the facilities and allocates the waste considering fuzzy waste quantities and capacities of waste-management facility in a multiperiod planning for integrated SWM. The model addresses uncertainty in waste quantity as well as uncertainties in the operating capacities of waste-management facilities. An example problem has been presented to demonstrate the usefulness of the proposed model in making the planning decisions related to SWM and achieving an efficient plan. The model is solved at different levels of membership function for the alternative solutions with respect to objective. The example problem reveals that the uncertainty in the waste quantity is likely to affect the planning for waste treatment/disposal facilities more as compared with the uncertainty in the capacities of the waste-management facilities. The relationship between increase in waste quantity and increase in the total cost involved in waste management is found to be nonlinear. The modeling results are useful for generating a range of decision alternatives under various economic conditions. They are valuable for analyzing the existing waste-management practices, the long-term capacity planning for the city’s waste-management system, and the identification of desired policies regarding waste generation and management.     

Fuzzy Analytical Hierarchy Process Risk Assessment Approach for Joint Venture Construction Projects in China

Research and practice show that construction joint venture (JV) activities in China are opportunities that can bring potential benefits but at the same time may generate many risks. While research has studied these risks and presented useful advice for managing individual risks, the methodologies used to analyze the risks were mainly qualitatively based, and there is a gap in using the quantitative method that can integrate a risk expert’s knowledge to assess the risks associated with JV projects. This paper sets up a hierarchy structure of the risks and then develops a fuzzy analytical hierarchy process (AHP) model for the appraisal of the risk environment pertaining to the JVs to support the rational decision making of project stakeholders. An empirical case study is used to demonstrate the application of the proposed fuzzy AHP model. It is concluded that the fuzzy AHP model is effective in tackling the risks involved in JV projects. The information presented in this paper should be shown to all parties considering JV business opportunities in China, and the proposed approach should be applicable to the research and analysis of risks associated with any type of construction projects.     

Selecting Appropriate Project Delivery System: Fuzzy Approach with Risk Analysis

The selection of an appropriate project delivery system that suits all project and owner needs is one of the key decisions to a successful project. Therefore, this decision should be made based on thorough analysis. In this paper, a fuzzy multiattribute decision-making (FMADM) model is developed. The model accounts for uncertainties and imprecision in the decision space as well as fuzziness in the nature of the decision attributes. The model utilizes fuzzy decision-making approach in order to evaluate the membership function corresponding to the utility of each project delivery alternative. Project delivery system alternatives are ranked using fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method based on their utility membership functions and by evaluating the distance of each project delivery alternative from fuzzy ideal solutions. In the TOPSIS method, alternatives are ranked based on their closeness coefficient (CC). In addition, the risk attitude of the decision maker is considered in the model by using derived utility membership functions corresponding to the risk attitude of the decision maker. The model is applied to a petrochemical project as a case study. In the case study, the model outcome that ranked Turnkey system as the best system conforms to the lessons learned by the decision maker from several past projects. Moreover, sensitivity analysis is done in the case study. The results show the significant value of the FMADM model for selecting appropriate project delivery system for projects.     

Fuzzy Optimization Model for Earthwork Allocations with Imprecise Parameters

Existing linear programming (LP) models of earthwork allocations in roadway construction assume that unit cost coefficients of earthwork activities and borrow pits/disposal sites capacities are certain and deterministic numbers. However in real-world problems there are naturally some uncertainties inherited in these values, which make it difficult to represent a single value as the candidate of entire possible values. This paper presents a fuzzy linear programming (FLP) model of earthwork allocations based on the fact of assuming unit cost coefficients and borrow pits/disposal sites capacities as fuzzy numbers while minimizing total earth-moving cost as an objective function. A method based on α cuts of a fuzzy set is used to take the uncertainty in borrow pits/disposal sites capacities into account. The uncertainty in fuzzy cost coefficients of the objective function and its effects on decision variables of the earthwork allocations model are also considered using the method presented by Chanas and Kuchta in 1994. Subsequently, a more general model is suggested which considers both uncertainties in borrow pits/disposal sites capacities and cost coefficients simultaneously. It is demonstrated that the presented FLP, compared to a deterministic LP, introduces a more robust solution; as the result of giving fuzziness to the uncertain parameters. Such a solution could be beneficial in real world decision making where uncertainties on resources and activities cost exist.     

Bayesian Framework for Characterizing Geotechnical Model Uncertainty

As any model is only an abstraction of the real world, model uncertainty always exists. The magnitude of model uncertainty is important for geotechnical decision making. If model uncertainty is not considered, the geotechnical predictions and hence the decisions based on the geotechnical predictions might be biased. In this study, a framework for characterizing geotechnical model uncertainty using observation data is proposed. The framework is based on the concept of multivariable Bayesian updating, in which the statistics of model uncertainty are updated using observed performance data. Uncertainties in both input parameters and observed data can be considered in the proposed framework. To bypass complex computational works involved in the proposed framework, a practical approximate solution is presented. The proposed framework is illustrated by characterizing the model uncertainty of four limit equilibrium methods for slope stability analysis using quality centrifuge test data. Parametric study in the illustrative example shows that both quality and quantity of the performance data could affect the determination of the model uncertainty, and that such effects can be systematically quantified with the proposed method.     

Method for Assessing Impacts of Parameter Uncertainty in Sediment Transport Modeling Applications

The predictions from a numerical sediment transport model inevitably include uncertainty because of assumptions in the model’s mathematical structure, the values of parameters, and various other sources. In this paper, the writers aim to develop a method that quantifies the degree to which parameter values are constrained by calibration data and the impacts of the remaining parameter uncertainty on model forecasts. The method uses a new multiobjective version of generalized likelihood uncertainty estimation. The likelihoods of parameter values are assessed using a function that weights different output variables on the basis of their first-order global sensitivities, which are obtained from the Fourier amplitude sensitivity test. The method is applied to Sedimentation and River Hydraulics—One Dimension (SRH-1D) models of two flume experiments: an erosional case and a depositional case. Overall, the results suggest that the sensitivities of the model outputs to the parameters can be rather different for erosional and depositional cases and that the outputs in the depositional case can be sensitive to more parameters. The results also suggest that the form of the likelihood function can have a significant impact on the assessment of parameter uncertainty and its implications for the uncertainty of model forecasts.     

Overview of the Application of “Fuzzy Techniques” in Construction Management Research

During the last decade, “fuzzy techniques” have been increasingly applied to the research area of construction management discipline. To date, however, no paper has attempted to summarize and present a critique of the existing “fuzzy” literature. This paper, therefore, aims to comprehensively review the fuzzy literature that has been published in eight selected top quality journals from 1996 to 2005, these being Journal of Construction Engineering and Management, ASCE; Journal of Management in Engineering, ASCE; Construction Management and Economics; Engineering, Construction and Architectural Management; International Journal of Project Management; Building Research and Information; Building and Environment; and Benchmarking: An International Journal. It has been found that fuzzy research, as applied in construction management discipline in the past decade, can be divided into two broad fields, encompassing: (1) fuzzy set/fuzzy logic; and (2) hybrid fuzzy techniques, with the applications in four main categories, including: (1) decision making; (2) performance; (3) evaluation/assessment; and (4) modeling. The comprehensive review provided in this paper offers new directions for fuzzy research and its application in construction management. Based on a comprehensive literature review on the applications of fuzzy set/fuzzy logic, and hybrid fuzzy techniques in construction management research, an increasing trend of applying these techniques in construction management research is observed. Therefore, it is suggested that future research studies related to fuzzy techniques can be continuously applied to these four major categories. Fuzzy membership functions and linguistic variables in particular can be used to suit applications to solving problems encountered in the construction industry based on the nature of construction, which are widely regarded as complicated, full of uncertainties, and contingent on changing environments. Moreover, hybrid fuzzy techniques, such as neurofuzzy and fuzzy neural networks, can be more widely applied because they can better tackle some problems in construction that fuzzy set/fuzzy logic alone may not best suit. For example, neural networks are strong in pattern recognition and automatic learning while fuzzy set and fuzzy logic are strong in modeling certain uncertainties. Their combination can assist in developing models with uncertainty under some forms of pattern. Finally, an increasing trend of applying fuzzy techniques in the building science and environmental disciplines is also observed; it is believed that the application of fuzzy techniques will go beyond the construction management area into these disciplines as well.     

Performance Evaluation and Uncertainty Measurement in Irrigation Scheduling Soil Water-Balance Approach

The present work aims at approaching the study of the performance and uncertainty associated with an irrigation scheduling method based on a soil-water balance. On a daily time step, a water-balance-based irrigation scheduling model has been developed. A Monte Carlo simulation of the irrigation scheduling model is developed using a series of actual daily weather data of evapotranspiration and precipitation and bootstrapping stochastic technique to resampling them. Performance evaluation measurements and their uncertainty are studied by means of several parameters: reliability, resiliency, vulnerability, total irrigation water allocation, total water loosed by deep percolation, and actual evapotranspiration/potential evapotranspiration rate along the growing season. The behaviors of 12 different types of soils (between coarse-textured soils and fine-textured soils) are compared using pedotransfer functions. Total available water (TAW) is the most important hydraulic property of the soil as far as irrigation scheduling performance is concerned. The statistical relationship between evaluation performance measures and TAW has been calculated. Soils with high values of TAW perform better. Rooting depth (Zr) and fraction of TAW that can be depleted from the root zone before moisture stress (p) are two variables that directly affect the TAW. It has been studied how evaluation performance measurements change when Zr and p change too. High values of Zr and p perform better too.     

Developing a Fuzzy Risk Allocation Model for PPP Projects in China

Equitable allocation of risks between the government and the private sector in concession agreement is essential to the success of public-private partnership (PPP) projects. The decision-making process, based on the established risk allocation principles expressed in linguistic terms, requires qualitative judgment and experiential knowledge of construction experts. However, it is subjective, partial, and implicit in actual application. This paper aims to develop a fuzzy synthetic evaluation model for determining an equitable risk allocation between the government and the private sector. By doing so, it assists the PPP project practitioners to transform the risk allocation principles in linguistic terms into a more usable and systematic quantitative-based analysis using fuzzy set. Twenty-three principles and influencing factors for risk allocation were identified through a comprehensive literature review. Nine critical risk allocation criteria (RACs) that evaluate the risk carrying capability of project participants were further identified, validated, and compiled based on the experts’ knowledge via face-to-face interviews. On the other hand, the weighting for each critical risk allocation criterion was determined through a two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the nine RACs. Based on the research findings, a fuzzy synthetic evaluation model was finally established to determine an equitable risk allocation between the government and the private sector.     

Risk Analysis for Dam Overtopping—Feitsui Reservoir as a Case Study

Risk and uncertainty analysis by mathematical and statistical methods is often used to assess systematic risks and uncertainties. This research presents the procedure and application of risk and reliability analysis to dam overtopping. Annual maximum series of peak discharges of Feitsui Reservoir in northern Taiwan are used to analyze five extreme flood events with different frequencies. The highest water levels of the five extreme flood events were computed by using reservoir routing and considering seven factors subject to uncertainty. Afterward, the overtopping risk of Feitsui Dam was assessed by five uncertainty analysis methods: Rosenblueth’s point estimation method (RPEM), Harr’s point estimation method (HPEM), Monte Carlo simulation, Latin hypercube sampling, and the mean-value first-order second-moment (MFOSM) method. The results show that values of overtopping risk computed by different methods are similar. One may apply some approximated methods (MFOSM, HPEM and RPEM) to avoid the computational burden by applying sampling methods. Furthermore, the accuracy of results by approximated methods compared with that by sampling methods may differ from case to case. The selection and application of the uncertainty methods depend upon the information availability of the model parameters and model complexity. One may need to examine the model parameters and model complexity before determining appropriate methods to be used in a study.     

CTAN for Risk Assessments Using Multilevel Stochastic Networks

Measuring projects’ cost and schedule risks in an integrated framework using simulation has several modeling challenges that have yet to be addressed by researchers. This paper presents a multilevel network modeling approach that aims to integrate a combination of different networks in one framework, and presents a computer simulation implementation to the cost and time risk assessment network (CTAN). The CTAN is an integrated network that includes uncertainties in the realization of the schedule logic, in activities durations, in project scope, and in cost. The simulation model is a decision support simulation system (DSSS) that currently consists of three modules: the CTAN, the stochastic decision trees, and the stochastic shortest/longest rout network. The CTAN-DSSS may be used in cost and schedule risk assessment. It completely integrates with other DSSS networks and deals with risks associated with cost, time, and scope at equal importance. The DSSS was verified by conducting several tests and validated by its extensive use for both undergraduate and graduate courses in Civil Engineering at the University of Calgary over the last three years.     

Risk Planning and Management for the Panama Canal Expansion Program

In April 2006, the Panama Canal Authority formally proposed a major expansion of the canal to increase its capacity and make it more productive, safe, and efficient. This proposal included cost and schedule estimates for completing the expansion and was supported overwhelmingly by the citizens of Panama in an October 2006 public referendum. Given the conceptual level of design at the time of the proposal and the inherent uncertainty in a project of this magnitude at the early stages of engineering, a comprehensive risk analysis was performed to develop a contingency model for the total expansion program cost and schedule. This contingency model is based on a Monte Carlo simulation of the cost and schedule estimates, taking into account the most significant risks identified for the project. The resulting model provides contingency assessments for duration and total cost and sensitivity analysis of the risks; it also allows for multiple scenario planning and ultimately supports overall risk management. This paper presents a project case study that focuses on the contingency model development and the resulting risk management and contingency resolution processes.     

Methodology for Analyzing Ranges of Uncertain Model Parameters and Their Impact on Total Maximum Daily Load Process

A methodology for analyzing uncertain parameter ranges prior to model calibration or uncertainty analysis is presented. The method considers parameters that exist in complex models and are typically difficult to set using site-specific data (i.e., parameters that have suggested ranges, national average ranges, or ranges set with land characteristic data). The method applies Monte Carlo runs and an interval-spaced sensitivity analysis to determine the parts of parameter ranges that will most likely cause unrealistic model results. An application of the method is presented using the Soil and Water Assessment Tool model as applied to the Cannonsville Reservoir system watershed for hydrology and sediment simulations. Results indicate that after parameter range reduction, the model output range was reduced by an order of magnitude, thereby reducing the uncertainty of the model and aiding the calibration effort. Sediment transport is difficult to monitor and model in its many stages of transport so significant uncertainty in the sediment erosion and transport parameters for this model still exist. This uncertainty will impact the application of the model for Total Maximum Daily Load development and management decisions.