Hybrid Methodology for Risk Assessment in Industrialised Execution of Precast Concrete Elements, with Applications on Post-Execution Inspection and Possible Integration of Digital Technologies

Abstract

Hidden works in concrete structures, either by assembling prefabricated elements or by executing monolithic elements in situ from simple or reinforced concrete, generate a high level of uncertainty in assessing quality and behavior in the long term. Their inaccessibility after completion limits the possibility of identifying defects and traditional risk analysis methods, based solely on the probability-impact relationship, do not sufficiently capture the specificities of production, assembly and casting processes.

In this context, the paper proposes a technical risk assessment methodology integrating qualitative criteria with quantifiable parameters, such as detectability, accessibility and variability of execution, differentiated for industrialised and in-situ elements.

The methodology includes an adaptive tracking datasheet designed to adjust the level of risk to actual site conditions and assessor experience. This makes it possible to identify the particularities of prefabricates, manufacturing tolerances, handling defects, non-conformities at joints, as well as the risks associated with monolithic elements such as segregation, insufficient compaction or quality variations of fresh concrete. The model is complemented by a post-execution- monitoring system based on non-destructive methods and predictive maintenance principles, whereby identified risks are converted into time-tracked parameters.

The results highlight that the proposed approach leads to a more realistic prioritisation of risks and optimisation of the quality control processes of hidden works, helping to increase the reliability of concrete structures, whether made from prefabricated or in-situ moulded elements.

In light of the evolution of digital technologies in construction, possible directions to extend the methodology are also discussed, such as the use of image processing for automated identification of non-conformities, the application of machine learning models for risk classification, or the integration of monitoring sensors for critical structural elements. These developments can help automate inspection steps and increase the objectivity and predictability of the assessment process, providing solid premises for improving quality control of hidden works in industrialised execution.