Design and application of methodologies for the quality control of buildings during the construction phase

The safety, durability and sustainability of modern buildings are essential in the construction industry, especially as structures become increasingly complex and must meet stringent standards. This thesis investigates advanced quality control and monitoring techniques, focusing on surface quality control and structural health monitoring (SHM), to improve the durability and reliability of building structures. Addressing the limitations of traditional inspection methods, the research introduces innovative methodologies based on artificial intelligence (AI), machine learning and sensor-based systems. The first part of the research focuses on surface quality control, emphasising the importance of assessing visible defects such as cracks, pitting, honeycombing and exposed reinforcement bars. These surface imperfections, while often considered aesthetic, can act as precursors to structural problems, allowing moisture and environmental factors to infiltrate, leading to corrosion and deeper structural deterioration. Traditional manual inspections, although widely practised, are labour-intensive, subjective and prone to inefficiency due to a shortage of qualified personnel. To overcome these challenges, this thesis proposes an automated system that combines image acquisition and processing with artificial intelligence-driven algorithms for defect detection and quantification. To train the neural networks, a customised dataset of concrete surface defects was created, which ensures high accuracy in identifying anomalies. The methodology takes measurement uncertainties into account, addressing potential defect recognition errors caused by noise or different background characteristics. The developed system is portable, enabling hands-on inspections in the field, and integrates with digital platforms to facilitate real-time monitoring during the building's life cycle. The research highlights how early detection and quantification of surface issues can mitigate long-term risks. The second part of the research focuses on SHM, with a special focus on curtain walls, which are increasingly common in modern high-rise buildings. These façades, often made of glass or lightweight materials, are crucial for the structural integrity and energy efficiency of a building. However, they are vulnerable to stresses such as weather loads and accidental impacts, requiring continuous monitoring to maintain safety and performance. This thesis introduces a new SHM methodology that integrates data from multiple sensors, including strain gauges and accelerometers, to acquire real-time information on structural behaviour. By correlating dynamic parameters with physical phenomena, the research identifies the main causes of potential failures, such as bolt loosening, thermal stresses or external impacts. Machine learning models were used to analyse some of the data acquired by the sensors, enabling early detection of anomalies and providing useful information for maintenance. The study also addresses compliance with international standards, such as EN 13830, ensuring that the monitoring solutions are in line with regulatory requirements. Experiments conducted on a weathered curtain wall demonstrated the effectiveness of the proposed SHM framework in identifying and predicting failure modes such as shock and vibration-induced stress. This research makes several key contributions to the field of construction monitoring. It develops an integrated system for surface defect detection, combining high-resolution images and AI-guided analysis, and extends the state-of-the-art by quantifying uncertainties in defect detection. Furthermore, it proposes a comprehensive SHM framework for curtain walls, exploiting multi-sensor data fusion and machine learning to improve predictive maintenance.