Integrated Risk Assessment in Construction Contracts: Comparative Evaluation of Risk Matrix and Monte Carlo Simulation on a High-Rise Office Building Project

This study investigates the application of two complementary risk analysis methods—risk matrix and Monte Carlo simulation—in the context of a large-scale office building construction project. The paper explores the theoretical and practical aspects of construction risk, focusing on how probabilistic and qualitative tools can support informed decision-making. Twelve key risks, including both threats and opportunities, were identified and quantified using expert judgment and historical data. The risk matrix provided an initial prioritization of risk severity and likelihood, while Monte Carlo simulations allowed for the modeling of uncertainty in cost outcomes across a probabilistic spectrum. The results indicate a high level of consistency between the methods, with both identifying value engineering as a dominant opportunity and network documentation errors as critical threats. Monte Carlo simulations further revealed that under proper risk management, the project is likely to avoid additional cost overruns with 60% certainty. This integrated approach provides practical insights for contractors and project managers seeking to enhance the robustness of risk assessment in complex construction environments.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-08-26
DOI
https://doi.org/10.3390/app15179371
Citations
3
Primary Topic
Construction Project Management and Performance
Type
article
Field-Weighted Citation Impact
6.32

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated Risk Assessment in Construction Contracts: Comparative Evaluation of Risk Matrix and Monte Carlo Simulation on a High-Rise Office Building Project

Anna Starczyk-Kołbyk, Izabela Jędras
3 citations
Applied Sciences
Construction Project Management and Performance
6.32
article

Integrated Risk Assessment in Construction Contracts: Comparative Evaluation of Risk Matrix and Monte Carlo Simulation on a High-Rise Office Building Project

Anna Starczyk-Kołbyk, Izabela Jędras
article en
3 citations

Abstract

This study investigates the application of two complementary risk analysis methods—risk matrix and Monte Carlo simulation—in the context of a large-scale office building construction project. The paper explores the theoretical and practical aspects of construction risk, focusing on how probabilistic and qualitative tools can support informed decision-making. Twelve key risks, including both threats and opportunities, were identified and quantified using expert judgment and historical data. The risk matrix provided an initial prioritization of risk severity and likelihood, while Monte Carlo simulations allowed for the modeling of uncertainty in cost outcomes across a probabilistic spectrum. The results indicate a high level of consistency between the methods, with both identifying value engineering as a dominant opportunity and network documentation errors as critical threats. Monte Carlo simulations further revealed that under proper risk management, the project is likely to avoid additional cost overruns with 60% certainty. This integrated approach provides practical insights for contractors and project managers seeking to enhance the robustness of risk assessment in complex construction environments.

Applied SciencesVol. 15(17)
Military University of Technology in Warsaw (PL)
Wojskowa Akademia Techniczna
Openalex Percentile: Top 3%
Construction Project Management and Performance
6.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.