سيدمحمدرضا خادمي

Structural health monitoring an‎d maintenance of concrete slabs using traditional approaches are predominantly based on the visual identification of apparent defects after damage has already occurred. In addition to imposing considerable maintenance costs, such approaches lack the capability to predict the progressive deterioration mechanisms, such as cracking induced by long-term shrinkage. Long-term concrete shrinkage is recognized as one of the principal factors contributing to durability degradation an‎d increased operational costs, particularly in concrete slabs with a high surface-to-volume ratio, thereby playing a critical role in the maintenance management of concrete structures. In this context, the development of a digital twin as an emerging technology enables a paradigm shift from reactive maintenance toward data-driven proactive maintenance strategies. In this study, with the aim of assessing the feasibility of developing a digital twin model for investigating damage caused by long-term shrinkage in an unreinforced concrete slab, a combined framework incorporating the B3 shrinkage prediction model, environmental data, an‎d material parameters extracted from a real-world project was employed. The digital twin architecture was developed through the integration of Python-based programming for shrinkage strain prediction, Building Information Modeling (BIM) in Autodesk Revit, an‎d parametric data exchange established via Dynamo. The input data include environmental conditions, slab geometric characteristics, mechanical properties of concrete, an‎d allowable tensile strain limits, while the analysis results are represented in a time-dependent manner within the three-dimensional digital twin model. The results demonstrate that the developed model is capable of predicting the temporal evolution of long-term shrinkage strain an‎d, by comparing it with the allowable tensile strain of concrete, identifying regions prone to cracking prior to the occurrence of physical damage. Based on the digital twin outputs, approximately 30% of the projected data indicate a future susceptibility to damage in the concrete slab. Furthermore, the comparative analysis of maintenance scenarios reveals that, on a unit-area basis, the digital twin–based proactive maintenance approach—despite requiring an initial investment for development an‎d implementation—leads to an approximate 69% reduction in total maintenance costs compared to the traditional maintenance method over a 25-year operational horizon. In addition, cash flow analysis an‎d engineering economic eva‎luations indicate that the cumulative costs of the proactive maintenance scenario over the entire 25-year period become economically equivalent, in terms of net present value, to the cumulative costs of the traditional maintenance approach at approximately the fourteenth year of operation. This finding implies that beyond this point in time, continued operation under the traditional maintenance strategy results in increasingly higher costs compared to the proactive maintenance approach. Overall, this study demonstrates that the digital twin can serve as an effective tool for reducing maintenance costs, optimizing resource allocation, an‎d enhancing managerial decision-making in concrete structures.

پايش سلامت سازه , دال بتني , جمع‌شدگي درازمدت بتن , دوقلوي ديجيتال , نگهداري پيشگيرانه , مدلسازي اطلاعات ساختمان , هزينه چرخه عمر

Structural Health Monitoring , Concrete Slab , Long-Term Concrete Shrinkage , Digital Twin , Preventive Maintenance , Building Information Modeling , Life Cycle Cost

Seyyed Mohammad Reza Khademi

Ali Akbar Shirzadi Javid