Enterprise AI Analysis
Renovation Decision Support System for Residential Buildings Based on the Analysis of Operational Documentation, BIM, and Machine Learning
This paper proposes a Renovation Decision Support System (RDSS) integrating a simplified Building Information Model (BIM), technical documentation, diagnostic data, and machine learning methods to support renovation planning in large-panel residential buildings. The system comprises five modules: BIMM, GTDM, BCAM, BPCM, and RDOM, managing data exchange through a Common Data Environment (CDE). It combines multi-criteria assessment with fuzzy inference and Mixed-Integer Linear Programming (MILP) for long-term optimization, considering budget, sequences, time horizons, and user preferences. This approach aims to enhance sustainable management, decision transparency, and data-driven planning.
Executive Impact
The Renovation Decision Support System (RDSS) promises significant improvements in efficiency, cost reduction, and extended asset lifespan for residential building management.
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Improvement in Renovation Planning Efficiency
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Reduction in Maintenance Costs
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Extension of Building Lifespan
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
BUILDING (GTDM)
→
BIMM/CDE
→
BCAM/BPCM/IoT
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DROM (Maintenance management)
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Core Modules Integrated in RDSS
Integrated Data Environment for Renovation
The RDSS concept addresses the challenge of fragmented data in building management by proposing a unified system. It integrates simplified BIM models with operational documentation and diagnostic data. This creates a shared knowledge base for technical condition assessment and automates renovation needs identification, especially crucial for large-panel housing stock in Central and Eastern Europe.
Enterprise Process Flow
Data collection (AI/Manual)
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Input data
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Model selection (ML)
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Model training
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Validation and testing
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Prediction
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Predicted Accuracy for Failure Risk (Conceptual)
Predictive Maintenance for Building Components
The BPCM module leverages Machine Learning (ML) to forecast degradation processes, failure risks, and future performance of building components. This allows for proactive maintenance, moving beyond reactive repairs. The system can predict the Remaining Useful Life (RUL) of elements like facades or installations, enabling timely interventions and optimizing resource allocation. Algorithms such as Random Forest Regression and LSTM networks are mentioned for regression and time-dependent data analysis.
Enterprise Process Flow
BIM/CDE (Data)
→
Residents (Preferences/Scheduling)
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Expert/Facility Manager (Costs/Budget, Repair sequence, Urgency/repair technologies, Time horizon)
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MILP model
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Building renovation scenarios
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Building repair schedule (Realization)
| Feature | Traditional Approach | RDSS Approach |
|---|---|---|
| Data Source | Fragmented, paper-based | Integrated BIM, operational, diagnostic |
| Decision Basis | Subjective expert assessments | Data-driven ML predictions, multi-criteria optimization |
| Planning Horizon | Short-term, reactive | Long-term, predictive, optimized |
| User Involvement | Limited | Incorporates resident preferences |
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Urgency Levels (UL) for Renovation Tasks
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Projected Annual Savings
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Annual Hours Reclaimed
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Implementation Roadmap
A phased approach to integrating the Renovation Decision Support System into your operations.
Phase 1: Data Digitization & BIM Integration (0-6 months)
Establish Common Data Environment (CDE), digitize existing technical documentation, and develop simplified BIM models for core building types. Integrate initial operational data from inspections.
Phase 2: Condition Assessment & ML Model Training (6-18 months)
Implement Building Condition Assessment Module (BCAM) for standardized evaluations. Begin collecting structured historical data for Building Performance and Condition Prediction Module (BPCM). Train initial ML models for degradation prediction based on available data.
Phase 3: Optimization & Scenario Generation (18-36 months)
Deploy Renovation Decision Optimization Module (RDOM) using MILP. Integrate resident preferences and expert input. Generate and evaluate long-term renovation scenarios, refining budget and scheduling constraints.
Phase 4: Continuous Improvement & System Expansion (36+ months)
Continuously update BIM with new operational data and ML predictions. Refine optimization algorithms. Explore integration with IoT sensors and advanced AI for real-time monitoring and adaptive planning.
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