A Model for Risk Assessment and Management of Construction Projects in Urban Conditions

Siemaszko, Agata; Grzyl, Beata; Kristowski, Adam

doi:10.1109/bgc-geomatics.2018.00008

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Another article [9] presented risk assessment and management in urban construction projects. The authors have been looking for new methods to improve the efficiency of investments related to the maintenance and operation of existing engineering structures.…”

Section: A Review Of Knowledge Of Metaheuristics In Engineeringmentioning

confidence: 99%

Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

Jakubczyk-Gałczyńska,

Siemaszko,

Poltavets

2024

Applied Sciences

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The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and financial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks and heuristic techniques to optimize decision-making processes in construction scheduling. As an innovative approach in order to enhance construction management, the functioning of biological, molecular, and physical objects and nervous systems is considered, applying bionic features to mimic their efficiency and precision, thereby optimizing construction processes and improving coordination and decision-making. Bayesian Networks are used for probabilistic analysis, and heuristic methods guide quick decision-making. The results demonstrate the effectiveness of Bayesian Networks and heuristic methods in data analysis and decision-making in construction engineering. The developed algorithm can be successfully applied to both erecting and planning construction projects.

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Section: A Review Of Knowledge Of Metaheuristics In Engineeringmentioning

confidence: 99%

Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

Jakubczyk-Gałczyńska,

Siemaszko,

Poltavets

2024

Applied Sciences

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“…The other work proposed a hybrid approach of system dynamics (SD) and discrete event simulation (DES) to quantify schedule risks in infrastructure projects without the purpose of considering acceleration or optimization [23]. One last study attempted to create a methodology using Bayesian Belief Nets and combining different input variables, such as the risk factors, their potential impact, and the mitigation action effects, to determine the impact on the time or cost of civil engineering projects in urban environments [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Largest risk impact DI add = Equations ( 22)- (24) show the calculations of the total impact on cost caused by overlapping risks. Equation ( 22) calculates the individual impact on the cost caused by each overlapping risk, and then Equation ( 23) calculates the cost subtotal for the overlapping degree.…”

Section: Duration Impact For Each Risk DImentioning

confidence: 99%

“…Equation ( 22) calculates the individual impact on the cost caused by each overlapping risk, and then Equation ( 23) calculates the cost subtotal for the overlapping degree. Equation (24) presents the total risk impact on the cost transferred to the schedule and determined by the overlapping degree with a value greater than zero.…”

Section: Duration Impact For Each Risk DImentioning

confidence: 99%

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Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

2023

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The construction industry has extensively applied the fast-tracking approach to the demanding need for the fast delivery of infrastructure projects. However, the fast-track strategy might be threatened by distinctive risks or changes in risk characteristics that emerge when activities are overlapped (overlapping risks). This article proposes a risk assessment simulation model to quantify the economic impact of overlapping risks on fast-track infrastructure projects. The model uses Monte Carlo simulation and a proprietary engine solution for the optimization procedure. It quantifies the overlapping risk impacts in the project duration and cost that could originate in three different overlapping degrees and evaluates the optimal overlapping degree to reduce the impact of the overlapping risks. The model demonstration used a commercial renovation project. The results suggest that overlapping risks have a high potential impact on the total cost, although with a high probability of attaining the target duration. Eight top risks affected the total duration, cost, or both. The optimum overlapping to reduce the economic impact and achieve the target project duration combines different overlapping degrees. This study contribution is a model for fast-track projects considering overlapping risks, their impact characteristic as a distribution, and the potential relationship between these risks.

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“…Many researchers [27][28][29][30][31][32][33][34][35][36][37] and ISO standard [11] state that in some publications the terms risk and uncertainty are treated identically or interchangeably, which is a mistake. According to Knight [28] and ISO standard [11], risk is related to the so-called measurable uncertainty.…”

Section: Reasons For Choosing Theory Of Possibility To Build the Modelmentioning

confidence: 99%

Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings under Conditions of Exposure to Risk

et al. 2019

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The paper discusses issues related to life cycle costs in construction. Life cycle cost is a key element in the assessment of environmental sustainability in construction and it provides a tool for the economic evaluation of alternative sustainability options exhibiting different capital, operating costs or resource usage. The authors reviewed selected models of estimating life cycle costs in construction, drew attention to the complex mathematical models developed so far, namely those which take into account only financial risks and those which involve the possibility of the influence of other risk factors and described the main assumptions accompanying the original model for estimating the whole life costs of buildings, including: reasons for choosing theory of possibility, division and parametrization of input data. The aim of this paper is to confirm the validity of the model structure assumptions adopted by the authors by comparing the originally selected fuzzy approach to calculating life cycle costs taking into account the risk with the probabilistic approach, as well as indicating the domain in which the probabilistic approach will complement the fuzzy approach chosen by the authors. The paper presents a comparison analysis of two approaches used in the authors’ model, a fuzzy and a probabilistic approach, recommended by the ISO standard 15686-5:2008. The authors used the Oracle Crystall Ball software in their simulations.

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A Model for Risk Assessment and Management of Construction Projects in Urban Conditions

Cited by 5 publications

References 8 publications

Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings under Conditions of Exposure to Risk

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