Detailed probabilistic construction estimating by Monte Carlo simulation

The complexity of construction operations and their associated uncertainties lead to a significant amount of risk in construction estimating. Conventional deterministic estimating cannot capture these uncertainties in a systematic and quantitative manner. Thus, a probabilistic approach is necessary to assess these risks. This paper presents a detailed probabilistic estimating using Monte Carlo simulation. The probabilistic estimating of a tunneling project is presented as an example application. Tunnel advance rates are estimated using detailed probabilistic scheduling of tunneling operations. Precedence activity networks for tunneling operations are constructed as functions of the chosen excavation and support method and the revealed geologic conditions (tunneling alternatives). The duration of tunneling activities is expressed by time-estimating equations, and their associated uncertainties are assessed by subjective assessment using the Perry & Greig method. Probabilistic scheduling is analyzed by Monte Carlo simulation performed in the ProbSched program. The results provide probability distributions of tunnel advance rates for all possible alternatives, which can be used to determine optimal excavation and support methods for the project.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171057/1/PGI-2004ThaiCivEngrNationalConvention-MonteCarlo_DOI_10.7302_3733.pdfDescription of PGI-2004ThaiCivEngrNationalConvention-MonteCarlo_DOI_10.7302_3733.pdf : Main articleSEL

Risk-sensitive Markov Decision Process for Underground Construction Planning and Estimating

This paper presents an application of dynamic decision making under uncertainty in planning and estimating underground construction. The application of the proposed methodology is illustrated by its application to an actual tunneling project—The Hanging Lake Tunnel Project in Colorado, USA. To encompass the typical risks in underground construction, tunneling decisions are structured as a risk-sensitive Markov decision process that reflects the decision process faced by a contractor in each tunneling round. This decision process consists of five basic components: (1) decision stages (locations), (2) system states (ground classes and tunneling methods), (3) alternatives (tunneling methods), (4) ground class transition probabilities, and (5) tunneling cost structure. The paper also presents concepts related to risk preference that are necessary to model the contractor’s risk attitude, including the lottery concept, utility theory, and the delta property. The optimality equation is formulated, the model components are defined, and the model is solved by stochastic dynamic programming. The main results are the optimal construction plans and risk-adjusted project costs, both of which reflect the dynamics of subsurface construction, the uncertainty about geologic variability as a function of available information, and the contractor’s risk preference

The economic value of geologic exploration as a risk reduction strategy in underground construction

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1984.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Bibliography: leaves 456-461.by Photios G. Ioannou.Ph.D

PERT/Cost and the VisionReader Project

http://deepblue.lib.umich.edu/bitstream/2027.42/191698/4/VisionReaderCase.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191698/1/VisionReaderCase.pdfDescription of VisionReaderCase.pdf : PERT/COST AND THE VISIONREADER PROJECT - CASE STUDYSEL

Quantitative Assessment of Risk Sensitivity in Construction Cost Estimating

Realistic construction estimating requires optimal decisions that not only minimize construction time and cost, but also address other important factors such as project risks. Probabilistic estimating is generally used to incorporate risk into estimated costs, the most common measure of which is the expected monetary value (EMV). Yet, EMV does not capture the way most people make decisions under uncertainty because it cannot reflect the decision maker’s risk sensitivity. This paper presents a probabilistic estimating model that can quantify and incorporate the contractor’s risk aversion. A highway tunneling project is presented to illustrate the application of the proposed model. Tunnel cost estimating is formulated as a risk-sensitive Markov decision process (MDP) where the contractor’s risk sensitivity is modeled by an exponential utility function. The contractor’s decisions depend on his risk aversion coefficient and the variability in tunneling costs, which in turn depend on geologic uncertainty. The risk sensitive MDP is solved by maximizing the expected utility value (EUV) of tunneling costs. The final results include the optimal excavation and support sequence, and the risk-adjusted tunneling costs for the project, as functions of the contractor’s risk sensitivity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171058/1/PGI-2004ThaiCivEngrNationalConvention-RiskSens_DOI_10.7302_3734.pdfDescription of PGI-2004ThaiCivEngrNationalConvention-RiskSens_DOI_10.7302_3734.pdf : Main articleSEL