AI in Construction Cost Management
AI technologies are revolutionizing how construction project costs are estimated, predicted, controlled, and optimized, offering significant improvements in accuracy and efficiency.
0.021
Mean Square Error (DBM-BPNN) for Cost Estimation
Future Directions in Cost Management
Key future directions include:
- Fully Automated Management: Systems for cost estimation and forecasting throughout the entire project lifecycle.
- Virtual Data Generation: Simulation-driven and digital twin-based approaches to alleviate data scarcity.
- Hybrid Data-Driven Models: Incorporating environmental, resource, market, economic, political, building type, and stakeholder factors.
- LLM-enabled Multimodal AI: Frameworks integrating textual documents, drawings, BIM, and real-time site information for early-stage cost reasoning and dynamic analytics.
AI in Construction Time Management
AI applications in time management focus on planning, scheduling, delay risk prediction, and optimization, crucial for ensuring projects are completed on schedule.
40.48
Average Project Duration Reduction (Case I) with GA
Future Directions in Time Management
Key future directions include:
- NLP- and LLM-based Approaches: Automated extraction and reasoning over schedule constraints from contracts, specifications, and planning documents.
- Multifaceted Delay Risk Prediction: Incorporating project location, duration, contract type, technical complexity, and climate patterns.
- Hybrid Optimization Models: Development of advanced models to improve efficiency.
- Autonomous AI Agents: Generative scheduling systems for adaptive, real-time time management under changing site conditions.
AI in Construction Safety Management
AI is transforming safety management by enabling automated monitoring, PPE detection, accident prediction, and hazard identification, significantly reducing risks on construction sites.
97
Accuracy for Safe/Unsafe Action Detection with Hybrid DL
Future Directions in Safety Management
Key future directions include:
- Robust Vision-Based Monitoring: Methods reliable under strong sunlight, occlusion, and complex worker interactions.
- Broader PPE Detection: Exploring detection of protective clothing, gloves, and goggles, combined with object-tracking.
- International Implications: Examining national differences for safety accident analysis and prediction.
- Multimodal AI and Vision-Language Models: Integrating video, sensor data, and textual safety rules for automated hazard identification and safety reasoning.
Cross-Domain Synthesis & Challenges
Exploring the shared methodologies and unique challenges of AI integration across cost, time, and safety management, along with future research directions.
High
Methodological Synergy Across Domains
Challenges in AI Adoption
Key challenges identified include:
- Data Quality and Availability: Limited historical records, small sample sizes, and difficulties in data collection compromise model accuracy and reliability.
- Practical Adaptability and Generalizability: Algorithms often depend on context-specific training data, making them difficult to apply across diverse project environments.
- Ethical Concerns and Privacy Issues: Use of surveillance cameras and location tracking raises questions about data security and worker consent.
- High Implementation Costs: Expenses for hardware, software, and technical expertise pose significant constraints.
