Sustainable development of BIM-enabled digital twin framework for monitoring indoor environmental quality: fuzzy analytic hierarchy process approach in educational buildings
Venkateswarlu Natakarani, Mahenthiran SathiyamoorthyPurpose
Existing BIM-enabled digital twins visualize indoor environmental quality but don't rank spaces by environmental risk across occupancy states. This study creates an occupancy-aware fuzzy analytical hierarchy process (FAHP) decision layer, integrated with a low-cost BIM-enabled DT, to generate ranked classroom priorities for intervention in educational buildings.
Design/methodology/approach
A four-layer DT architecture (data acquisition, transmission/storage, modeling/integration, analysis/visualization) was implemented using Arduino UNO R4 Wi-Fi microcontrollers and low-cost sensors (temperature, humidity, CO, NO2, PM2.5, CO2) across five classrooms in an educational building in Vellore, India, under both occupied and unoccupied conditions. Data were transmitted via ThingSpeak and visualized in a Revit/Dynamo BIM model. Three experts pairwise compared six IEQ parameters using Buckley's geometric-mean FAHP to derive crisp weights (consistency ratio = 0.087). Final classroom scores combined weighted, normalized occupied and unoccupied values (a = 0.5), with sensitivity analysis across a = 0.3–0.9, and a two-tier decision rule reconciled relative FAHP rankings with absolute exceedance of WHO/ASHRAE guidelines.
Findings
PM2.5 (0.428) and CO2 (0.231) received the highest weights. Classrooms 2 (0.2895) and 3 (0.284) ranked highest (worst relative IEQ) and were consistently top priorities across most a values; all five classrooms exceeded WHO CO guidelines at some point.
Practical implications
Linking real-time IoT data to the DT supports demand-controlled ventilation and timely interventions, reducing unnecessary energy use and pollutant exposure, supporting SDG 7 and SDG 13.
Originality/value
The study introduces a novel occupancy-differentiated FAHP weighting scheme that links BIM-DT sensor data directly to actionable classroom prioritization, thereby addressing the gap between IEQ visualization and facility management decision-making.