Jul 2026· Jurnal Teknik Sipil dan Arsitektur· Vol 31, pp. 308-323· 0 citations
Abstract
Probabilistic risk management has become central to delivering safer and more resilient building projects under growing cost, schedule, and sustainability pressures. This study synthesizes how probability-based approaches have been applied in building-related risk research and identifies unresolved gaps that limit their practical impact. Using a PRISMA-guided systematic literature review of the Scopus database, we screened 165 records and qualitatively analyzed 39 Q1–Q4 journal articles published between 2015 and 2025. The review applies a multi-dimensional framework that classifies studies by risk type, project type, life-cycle stage, building type, methodology, and geographic context. Quantitative designs dominate, particularly Monte Carlo simulation and Bayesian or Dynamic Bayesian Network models, which are frequently embedded in BIM-enabled and Digital Twin environments for cost, safety, and seismic performance assessment. Evidence shows that construction and financial risks, commercial and residential buildings, and construction and operation–maintenance stages attract most scholarly attention, while pre-construction phases, infrastructure assets, and developing-country contexts remain under-explored. The review develops an integrated conceptual map that links probabilistic techniques with risk categories and project settings, and highlights the citation influence of seminal contributions. It also reveals methodological imbalances (limited mixed-methods and participatory studies) and weak integration of probabilistic risk assessment with sustainability and resilience objectives. Future research should extend probabilistic risk models to under-represented geographies and project types, exploit hybrid quantitative–qualitative and AI-enhanced approaches, and embed climate and circular-economy considerations into construction risk decision-support tools.
Construction projects are characterized by high levels of uncertainty that may affect project cost, quality, and schedule performance. Effective risk management is therefore essential to determine contingency costs that adequately reflect project risks. This study aims to identify dominant construction work packages based on risk exposure and estimate risk-based contingency costs for the Main Building Construction Project of the Gianyar Regency Government Center. Project risks were identified through a literature review and Work Breakdown Structure (WBS), validated by experts, and compiled into a risk register. The Probability Impact Matrix (PIM) was used to identify dominant risks, while the Expected Monetary Value (EMV) method was applied to estimate contingency costs for cost (EMVc), quality (EMVq), and schedule (EMVt) aspects. These three aspects were then integrated to determine the overall contingency cost. The results identified ten high-risk work packages, including concrete, reinforcement, steel roof trusses, masonry, sanitary, signage, roof covering, plumbing, road, and softscape works. The EMV analysis produced contingency values of 6.22% (EMVc), 5.95% (EMVq), and 0.70% (EMVt), resulting in an integrated contingency cost of IDR 28.92 billion, equivalent to 12.87% of the project contract value. The findings indicate that contingency cost requirements are primarily influenced by cost and quality risks, providing a more objective basis for contingency reserve allocation during project execution.
Ni Luh Ayu Ariati, Dewa Ketut Sudarsana, G.A.P. Candra Dharmayanti· Enrichment: Journal of Multi...· 0 citations
This study presents a systematic taxonomic review of risk modelling and assessment methods in construction projects over the past 35 years (1990–2025). Through a structured four-stage process, 91 peer-reviewed articles from 15 leading journals were analysed. The taxonomic approach enabled the classification and mapping of methods according to chronological evolution, study type, authorship patterns, and focus areas, while thematic analysis was employed to synthesise key themes, trends, and research gaps. The review examines publication trends, geographical distribution of research contributions, and methodological developments. The findings reveal that the probability-impact (P-I) model remains the dominant approach, despite its well-documented limitations in capturing risk interdependencies and their cascading effects on project quality and overall performance. Fuzzy Set Theory (FST), Analytic Hierarchy Process (AHP), and Monte Carlo Simulation (MCS) emerged as the most frequently adopted techniques. The analysis demonstrates a clear evolution in the field: from predominantly basic probabilistic methods in the 1990s to increasingly sophisticated hybrid, fuzzy logic-based, and AI-enhanced approaches after 2010. Notwithstanding these advancements, significant gaps persist, particularly the lack of integrated frameworks capable of simultaneously addressing risks across multiple project objectives—cost, time, quality, and performance. This review synthesises the state of knowledge in the field, identifies persistent theoretical and practical shortcomings, and offers a comprehensive roadmap for future research. Key directions include the development of machine learning applications, dynamic modelling techniques, and holistic multi-objective risk assessment frameworks to better align risk management theory with the complex realities of modern construction projects.
Building construction sites are dynamic systems in which safety decisions interact with time, cost, productivity, equipment movement, and spatial constraints. This systematic review examines how building-construction-stage safety is represented in optimization-based and optimization-linked decision studies published between 1 January 2016 and 30 June 2026. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-informed workflow used searches of the Web of Science Core Collection and IEEE Xplore, supplemented by Google Scholar and backward-citation checks. Seventy-nine studies met the core inclusion criterion, which required safety to appear as a quantified objective, constraint, evaluation metric, decision criterion, or prediction target. Studies were classified by problem type, safety role, method family, digital integration, and validation evidence. The synthesis identifies a problem-type-dependent formulation pattern: site-layout and scheduling studies mainly optimize safety or exposure objectives; crane/lifting studies distribute safety across constraints, objectives, and decision criteria; risk-decision studies use criteria or metrics; and prediction studies tune models whose targets are safety or risk outcomes. The core corpus is concentrated in site-layout and crane/lifting studies, whereas temporary works, monitoring-to-intervention, and construction-stage emergency response are less often formulated as optimization problems. Strict real-site/field evidence was identified in 7 of 79 studies, with an upper sensitivity bound of 11. Future research should prioritize transparent metrics, benchmarks, field validation, and closed-loop workflows.
J. Seo, JinHwan Kim, Gyeonggyu Park et al.· Buildings· 0 citations
This review examines how uncertainty-informed scheduling strengthens the credibility of project time forecasting in complex delivery environments. Its purpose is to assess the limitations of fixed-date planning and to explain how probabilistic analysis improves understanding of completion risk, float consumption, and critical-path instability. The study adopts a narrative review approach, synthesising scholarly and professional literature on deterministic scheduling, schedule risk modelling, Monte Carlo simulation, network sensitivity, governance, and data-led project control. Emphasis is placed on how probability-based outputs can support more defensible planning, contingency allocation, and executive decision-making in uncertain operating contexts.
The review finds that deterministic schedules remain necessary for defining work logic, dependencies, milestones, and baseline accountability, but they are analytically constrained when used as instruments of prediction. Single-point duration estimates, static critical paths, and nominal float values often conceal uncertainty arising from procurement delays, productivity variation, design development, resource limitations, stakeholder interfaces, and systemic operational risk. Monte Carlo simulation is shown to provide a more rigorous basis for schedule assurance by generating completion-date distributions, confidence levels, sensitivity rankings, and risk-driver insights. The study further establishes that float is not a permanent reserve but a dynamic network property that may erode rapidly as near-critical paths emerge and project assumptions change.
The review concludes that probability-based schedule analysis should be institutionalised as a governance practice rather than treated as an optional technical exercise. It recommends improved schedule-quality assurance, transparent modelling assumptions, sensitivity-based monitoring, integration of real-time performance data, and stronger executive capacity to interpret probabilistic evidence. These measures can enhance schedule realism, strengthen accountability, and improve delivery confidence across complex projects.
Dominic Feboh, Abeebat Ajirotutu, Ogochukwu T Izuchukwu· International Journal of Mul...· 0 citations
Construction delays remain a persistent challenge in Australian construction projects, contributing to cost escalation, disrupted work sequences, contractual claims, and reduced confidence in project delivery. Although delay causes have been widely investigated, existing studies often provide broad factor lists and prioritise risks using single-dimension or inconsistent scoring approaches. This limits guidance on which delay risks should receive priority attention when project teams face constrained time, cost, and management resources. This study addresses this limitation by quantifying and prioritising 22 validated delay risk factors in Australian construction projects. Probability of occurrence and schedule impact were evaluated as separate judgement dimensions before being integrated into an overall measure of risk criticality. Data were collected from 48 experienced Australian construction professionals. A dual-dimension Fuzzy Best–Worst Method was applied to derive separate ratio-scale weights for probability of occurrence and schedule impact, with dimension-specific consistency screening used to improve judgement reliability. The resulting weights were integrated using a probability–impact formulation and mapped onto a 5 × 5 Probability–Impact Matrix through quantile-based discretisation. A 10,000-iteration Monte Carlo robustness analysis was subsequently conducted to assess the stability of the resulting rankings under alternative expert-selection and weighting scenarios. The results indicate that the delay risks perceived by the participating professionals as having the highest combined probability and schedule impact are predominantly governance-, approval-, and coordination-related, particularly owner late decisions, change-approval delays, owner requirement changes, cost-estimation deficiencies, design-approval delays, and inadequate planning. The Monte Carlo analysis further indicated that the principal risk rankings remained relatively stable under variations in expert aggregation. Overall, the integrated FBWM–PIM framework provides a structured and practically interpretable approach for eliciting and prioritising expert perceptions of construction delay risk and translating them into an actionable classification tool for allocating limited risk management resources.
Faranak Zagia, S. Kajewski, S. Omrani et al.· Buildings· 0 citations
Construction projects are inherently complex and uncertain due to the involvement of multiple stakeholders, dynamic site conditions, financial constraints, and technical challenges. These uncertainties expose projects to various risks that may adversely affect cost, time, quality, safety, and overall project performance. Risk analysis plays a crucial role in identifying, assessing, and mitigating such risks to improve decision-making and project success. This review paper presents a comprehensive analysis of risk management concepts in construction projects, focusing on risk identification techniques, qualitative and quantitative risk analysis methods, and commonly encountered risks across different project phases. The study also reviews recent literature on risk analysis models, tools, and methodologies adopted in construction project management. Finally, gaps in existing research and future research directions are discussed to enhance the effectiveness of risk analysis practices in the construction industry.
Watangi Sheetal Rajendra, Patil J. A.· World Journal of Advanced En...· 0 citations