Successful Delivery of Infrastructural Projects： Epistemic Overview of Cost Risk and Uncertainties

The process of decision making and risk analysis are essential tasks along the construction project cycle. Over the years, construction practitioners and researchers have used various methods, tools and techniques to evaluate risk and assist in making more concise decisions. Most practitioners, however, rely on their expert judgment, past experience, intuition, acquired and accumulated knowledge and gut feelings to make decisions. Aleatory （natural, heterogeneity and stochasticity） and epistemic （subjective, ignorance） are the two major types of uncertainties observed in natural sciences. Practitioners traditionally deal with aleatory uncertainty through probabilistic analysis based on historical data （frequentist approach）; and epistemic uncertainty, on the other hand, handled through the Bayesian approach which has limitations since it requires a priori assumption. This paper reports the application of the DST （Dempster Shafer Theory） of evidence to determine the most critical risk factors affecting project cost contingencies using their epistemic probabilities of occurrence. The paper further discuses how these factors can be managed to enhance successful delivery of infrastructural projects. It uses the mixed methodology, with data gathered through structured questionnaires distributed to construction clients, contractors, professionals and experts in the built environment. The research revealed that design risk, financial risk and economic risk were most important cost risk categorizations. In particular, scope changes, incomplete scope definition, incomplete design, changes in specification, micro and macroeconomic indicators and delayed payment problems were identified as the most important risk factors to be considered during the cost contingency estimation process, hence successful delivery of infrastructural projects. The paper concludes by recommending modalities for managing the contingency evolution process of risk estimation to enhance successful delivery and management of infrastructural projects.

Reliability‑Based Analysis of a Caisson Breakwater with the Application of Bayesian Inference

Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance of structures have epistemic or aleatory uncertainties.Furthermore,sliding failure is one of the most important failure modes of caisson breakwaters.In most previous studies,for assessment purposes,uncertainties,such as wave and wave period variation,were ignored.Therefore,in this study,Bayesian reliability analysis is implemented to assess the failure probability of the sliding of Tombak port breakwater in the Persian Gulf.The mean and standard deviations were taken as random variables to consider dismissed uncertainties.For this purpose,the frst-order reliability method(FORM)and the frst principal curvature cor-rection in FORM are used to calculate the reliability index.The performances of these methods are verifed by importance sampling through Monte Carlo simulation(MCS).In addition,the reliability index sensitivities of each random variable are calculated to evaluate the importance of diferent random variables while calculating the caisson sliding.The results show that the reliability index is most sensitive to the coefcients of friction,wave height,and caisson weight(or concrete density).The sensitivity of the failure probability of each of the random variables and their uncertainties are calculated by the derivative method.Finally,the Bayesian regression is implemented to predict the statistical properties of breakwater sliding with non-informative priors,which are compared to Goda’s formulation,used in breakwater design standards.The analysis shows that the model posterior for the sliding of a caisson breakwater has a mean and standard deviation of 0.039 and 0.022,respectively.A normal quantile analysis and residual analysis are also performed to evaluate the correctness of the model responses.

State dependent parameter method for importance analysis in the presence of epistemic and aleatory uncertainties

For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP) based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marginal probability density function(PDF) of the epistemic variable and the conditional PDF of the aleatory one at the fixed epistemic variable,the epistemic and aleatory uncertainties are propagated to the response of the structure firstly in the presented method.And the computational model for calculating the importance measures of the epistemic variables is established.For solving the computational model,the high efficient SDP method is applied to estimating the first order high dimensional model representation(HDMR) to obtain the importance measures.Compared with the direct Monte Carlo method,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared with the existing approximation method,because it is suitable not only for the linear response functions,but also for nonlinear response functions.Several examples are used to demonstrate the advantages of the presented method.

Uncertainty Quantification for Structural Optimal Design Based on Evidence Theory

Uncertainty design can take account of aleatory and epistemic uncertainty in optimal processes.Aleatory uncertainty and epistemic uncertainty can be expressed as evidence theory uniformly, and evidence theory is used to describe the uncertainty. Transferring and response with evidence theory for structural optimal design are introduced. The principle of response evaluation is also set up. Finally, the cantilever beam in a test system is optimized in the introduced optimization process, and the results are estimated by the evaluation principle. The optimal process is validated after the optimization of beam.

A novel method for importance measure analysis in the presence of epistemic and aleatory uncertainties

For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establish a novel deterministic transition model for auxiliary variables, distribution parameters, random variables, failure probability, then to propose the improved importance sampling （IS） to solve the transition model. Furthermore, the distribution parameters and auxiliary variables are sampled simultaneously and independently;therefore, the inefficient sampling procedure with an＇＇inner-loop＇＇ for epistemic uncertainty and an＇＇outer-loop＇＇ for aleatory uncertainty in traditional methods is avoided. Since the proposed method combines the fast convergence of the proper estimates and searches failure samples in the interesting regions with high efficiency, the proposed method is more efficient than traditional methods for the variance-based failure probability sensitivity measures in the presence of epistemic and aleatory uncertainties. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the proposed method for structural systems with both epistemic and aleatory uncertainties.