In September 2020, a part of a quay wall collapsed along the Grimburgwal in Amsterdam, the Netherlands. The collapse of the Grimburgwal raised concerns about the safety of 200 km of structurally similar quay walls in Amsterdam, which have existed since the end of the 16th century. The quay walls are not only heavily used, but the structures are also part of the UNESCO heritage site Seventeenth-Century Canal Ring Area of Amsterdam inside the Singelgracht. Many Dutch cities, besides Amsterdam, such as Leiden and Delft, face similar issues, as the Netherlands has more than 1700 km of historic quay walls. Therefore, a methodology is required to assess the condition of the historic quay walls so that maintenance and interventions can be prioritised effectively to prevent similar collapses in the future.

However, assessing the structural condition of these historic quay walls is a complex task. The structures, typically consisting of a masonry wall supported by a timber floor, timber beams, and timber piles, present several challenges: (1) the total length of quay walls to be assessed is substantial; (2) their geometrical, material, and loading characteristics exhibit significant spatial variability; (3) their behaviour is influenced by soil-structure interaction; and (4) they are subject to fluid-structure interaction.

In this research, a vibration-based monitoring campaign was conducted to investigate whether vibration-based monitoring can be applied to assess the condition of quay wall structures. During the experiment, controlled structural damage was imposed on a quay wall along the Nieuwe Herengracht in Amsterdam (constructed in 1861) by isolating a segment of the structure and sequentially cutting part of its timber foundation piles (see attached image). Imposing controlled structural damage to this quay wall was feasible, as the Municipality of Amsterdam started the renewal of this quay wall immediately after the experiment.

Acceleration measurements, complemented by inclinometer data and total-station displacement measurements, were collected to identify and compare the dynamic features of the structure (i.e., natural frequencies, mode shapes, and damping ratios) before and after isolating the section, as well as before and after cutting part of the timber foundation piles. Based on this comparison, this study will show whether vibration-based monitoring can detect variations in the quay wall's dynamic behaviour due to damage (i.e., common quay wall failure mechanisms induced during the experiment) and whether this information can be used as a proxy for structural health assessment.

In 1461, Sultan Mehmed the Conqueror laid the foundations of the Tersane-i Amire in Istanbul's Golden Horn. The shipyard was continuously expanded by successive Ottoman sultans over the centuries with the addition of slipways, warehouses, a torpedo facility, and a foundry for propellers. It also developed an ecosystem with a range of administrative and public buildings, including a hospital, hammam, school, and mosque. Tersane-i Amire remained in operation until the 1970s. Afterwards, it was partially moved and partially closed, with its historic slipways and buildings protected as a part of the city’s industrial heritage. In 2019, a project was initiated to transform the whole complex into a tourism and lifestyle destination by restoring the historical slipways, workshops, and buildings while preserving their original form.

This study aims to control the reliability of a finite element model constructed for a historic building complex formed by nine side-by-side buildings within Tersane-i Amire. The structural system of the complex was formed by masonry walls, and throughout its life, some alteration was performed. Lately, it was transformed to its original system, and a numerical model has been constructed to assess its seismic performance. The validation of the constructed numerical model was required by the owner due to the inherent difficulties related to the complexity of the system and the lack of material test opportunities due to the historical value of the building. Therefore, an ambient vibration survey (AVS) was applied to the complex to reveal its dynamic properties. The extracted properties were compared to those obtained by eigenvalue analysis. The use of experimentally obtained dynamic properties in improving the constructed finite element model was suggested.

Accurate prediction of the structural temperature field is crucial for the static and dynamic monitoring of engineering structures, with particular significance for heritage buildings where material preservation is paramount. The complex, time-lagged, and non-linear relationship between external air temperature and structural thermal response poses a significant challenge for traditional empirical or statistical methods. This study proposes a framework utilizing statistical regression and advanced recurrent deep learning models to accurately predict structural temperature based on external data. A key focus is the efficient generation of input features to capture delayed and cumulative thermal effects. The methodology is applied in a case study to predict temperatures at various points within a historic basilica. The primary objectives are to remove temperature-induced effects from structural monitoring data for a more accurate assessment of the building's performance and to provide a robust method for imputing missing data. A comparative analysis demonstrates the performance of the models, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results offer a reliable and precise methodology for selecting the optimal approach for structural temperature field estimation and data correction in the assessment of heritage structures.

Civil structures such as buildings, roads, railways, bridges, tunnels, and dams are present in every society, and the safest and most durable ones are those that are properly managed and maintained. Health monitoring plays an important role in ensuring safety, but engineers still depend on traditional inspection tools like crack gauges or comparator cards, which are slow, labour-intensive, and differ based on the inspector's judgment. There is also no proper tool that systematically analyses this data or suggests solutions based on standard codes using AI. Considering these limitations, this project aims to develop an AI model that processes images to classify whether a defect is structural or non-structural, identify the type of defect, assign a severity score where 0 is worst and 100 is best, suggest causes and remedies using an LLM, and estimate the remaining service life. Literature supports the need for such a system, as studies have focused on deep-learning-based detection of concrete damage, CNN-based comparison between traditional and AI inspection, and 3D scanning with machine learning for defect detection. Surveys on NDT tools like UPV and X-ray show improved efficiency over manual methods but still remain time-consuming and less precise.

Most research concentrates on a single defect such as cracks, and although some models can detect or localize them, there is still a major gap in systems that quantify severity or provide causes and repair suggestions. Many approaches stop at detection without offering guidance for maintenance, making it difficult to translate inspection data into decisions. To address this, datasets in this project are collected from public sources and field surveys, followed by preprocessing such as histogram equalization and median filtering. Images are then manually labelled into structural and non-structural categories and further classified by defect type. An image classification model is trained on this dataset to automatically detect and classify defects. Severity scoring is done using the formula Intensity × Extent, where intensity depends on measurable parameters like crack width or spall area, and extent depends on defect length or percentage affected. The model uses an LLM aligned with Indian Standard codes to suggest possible causes and remedies and predicts remaining service life based on severity. The novelty of this work lies in combining multiple functions that previous studies treat separately: multi-defect classification, quantitative severity scoring, LLM-based engineering interpretation, and service-life estimation. No existing system integrates all these components into a single workflow dedicated to structural inspection.

Buildings are long-term investments, and regular monitoring helps extend their lifespan and prevent failures. This project aligns with SDG 9, SDG 11, and SDG 12 by supporting safer, more resource-efficient, and sustainable infrastructure management. By bringing together defect detection, severity assessment, interpretation, and life prediction, the work addresses limitations of manual inspections and gaps in current automated tools. It supports better maintenance planning and contributes to a more comprehensive AI-based structural health-monitoring framework.

Urban seismology focuses on analysing subsurface structures and enhancing seismic risk management in the cities. Recent advancements, including digital seismic stations and ambient vibration analysis, have increased interests in non-natural seismic signals due to traffic, subways, and other human activities. On the other hand, historic monuments are cultural assets of each society, their importance stems from the variety of roles they play in urban landscape. Thus, their preservation is significant for development and sustainability of cities. This research focuses on the implementation of Structural Health Monitoring system at the Four Days Memorial located in Naples, which is positioned on an isolated podium supported by four elastomeric isolators and four sliding bearings. Firstly, modal analysis is performed to evaluate the dynamic characteristics of the monument before and after implementation of base isolation system. Furthermore, to assess dynamic behaviour of the monument during seismic activities and ambient vibrations generated by vehicular traffic and the adjacent Metro Line, triaxial velocimeters are installed on the foundation slab and the isolated podium. The collected data are analysed in accordance with the standards to determine the maximum response of the monument to various vibrations and to compare them with the thresholds established by the standards for short- and long-term monitoring. The results of data processing indicate that the vibration source significantly affects the dynamic response of the monument. Additionally, dominant frequency components of the vibrations due to earthquakes, subway transit and vehicular traffic are compared. Dynamic response at foundation level and isolated podium remains within the allowable limits for permanent and transient vibration caused by numerous earthquakes occurring recently due to the nearby Phlegraean Fields bradyseism activities. Finally, time sensitivity of the monument response to traffic-induced vibrations are presented.