Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

Baudrit, Cédric; Taillandier, Franck; Tran, Thi Thuy Phuong; Breysse, Denys

doi:10.1111/mice.12391

Cited by 23 publications

(11 citation statements)

References 56 publications

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“…Ms-ReRO [4] is an uncertainty analysis method specifically designed for this purpose, based on hierarchical probabilistic relational modeling (HPRM) [45] and MOO, which affords an assessment of the global risks and opportunities at the central government (top) scale triggered by a plan's implementation at the municipal (local) scale. In this methodology, integrated planning implementation is represented as a hierarchical system of systems that are connected by relational contracts, and risks and opportunities are derived as the bottom-up cascading impacts produced by the actions performed at a local scale.…”

Section: Step 3: Resilient Planning Ii-implementation Planningmentioning

confidence: 99%

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Salas

Piqueras

2020

IJERPH

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Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.

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Section: Step 3: Resilient Planning Ii-implementation Planningmentioning

confidence: 99%

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Salas

Piqueras

2020

IJERPH

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“…The study of Ding et al [38] coupled ontology and semantic web technology in a BIM environment to manage construction risk knowledge. Apart from enhancing general risk management procedures, other practical applications of ontology in the construction domain include knowledge sharing [39][40][41], information extraction [42][43][44], and performance analysis [45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Resilient Scheduling as a Response to Uncertainty in Construction Projects

2021

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Complex construction projects are developed in a dynamic environment, where uncertainty conditions have a great potential to affect project deliverables. In an attempt to efficiently deal with the negative impacts of uncertainty, resilient baseline schedules are produced to improve the probability of reaching project goals, such as respecting the due date and reaching the expected profit. Prior to introducing the resilient scheduling procedure, a taxonomy model was built to account for uncertainty sources in construction projects. Thence, a multi-objective optimization model is presented to manage the impact of uncertainty. This approach can be described as a complex trade-off analysis between three important features of a construction project: duration, stability, and profit. The result of the suggested procedure is presented in a form of a resilient baseline schedule, so the ability of a schedule to absorb uncertain perturbations is improved. The proposed optimization problem is illustrated on the example project network, along which the probabilistic simulation method was used to validate the results of the scheduling process in uncertain conditions. The proposed resilient scheduling approach leads to more accurate forecasting, so the project planning calculations are accepted with increased confidence levels.

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“…On the other hand, degradation data (database) used for the inference procedure are usually collected from sensors, destructive or nondestructive inspections; they are subjected to a random error. The presence of such random error can be due either to the quality of the adopted measurement protocol, the regression law between measurement and degradation parameter (Baudrit, Taillandier, Tran, & Breysse, 2019), or its type combined with the environment conditions. Besides this, this random errors in many cases lead to negative increments that are not consistent with the monotony of the Gamma Process.…”

Section: Introductionmentioning

confidence: 99%

A Perturbed Markovian process with state‐dependent increments and measurement uncertainty in degradation modeling

Oumouni

Schoefs

2021

Computer aided Civil Eng

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In structural reliability, the Markovian cumulative damage approaches such as Gamma process seem promising to model a nonreversible deterioration that involves gradually over time with small time increments. However, in many degradation phenomena, its evolution depends on the level of the degradation rather than the increments of time. Further, the measured data are usually collected with random error whose dispersion depends on the current degradation level. Therefore, the deterioration model should take in consideration such dependency and uncertainty for more accurate prediction. In this paper, a new statistical, data-driven state-dependent and perturbed model is proposed to model the state dependence (hidden level-degradation) both in temporal increments and measurement uncertainty. The construction of the degradation model will be discussed within an application to the pitting corrosion and synthetic data. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. Algorithm and estimates are proposed to compute the parameters of the model, the hidden state, and failure probabilities.

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Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

Cited by 23 publications

References 56 publications

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Resilient Scheduling as a Response to Uncertainty in Construction Projects

A Perturbed Markovian process with state‐dependent increments and measurement uncertainty in degradation modeling

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