Risk Identification and Prioritization in Construction Projects: A Probability–Impact Assessment and Risk Mitigation Framework

Construction projects face interconnected uncertainties associated with design, scheduling, costs, resources, quality, environmental conditions, stakeholder coordination and regulatory approvals. This paper develops a structured framework for identifying, classifying and prioritizing construction project risks using a probability–impact (P–I) assessment and linking the resulting priorities to proportionate mitigation actions. The analysis is grounded in the supplied M.Tech. thesis, particularly its risk taxonomy, sample quantitative scoring table, probability–impact matrix, risk exposure ranking and mitigation planning discussion. The thesis describes a smart residential township case involving 250 housing units, a planned 24-month duration and a stated budget of ₹120 crore; it also identifies risks including approval delays, budget overrun, scope change, resource unavailability, poor communication, monsoon disruption, price escalation and smart-system integration issues. However, the thesis does not provide a transparent underlying dataset, respondent-level records, scoring protocol or independently verifiable project documentation for all displayed values. Accordingly, this paper treats those scores and case details as thesis-reported illustrative case material, not as independently validated empirical results. The proposed framework uses a five-point likelihood and impact scale, a transparent risk exposure score, category-based classification, explicit ownership, response selection, residual-risk review and periodic reassessment. The paper also critically discusses the limitations of multiplying ordinal ratings, the danger of treating a heat map as a precise quantitative model, and the need to preserve low-probability/high-consequence risks from being masked by average scores. Its contribution is a practical, auditable workflow for construction teams, while recognizing that empirical validation requires verified project records and structured expert or survey data.