Research Interests
Research Interest 1: Non-linear elastodynamics in compressible solids
In structural design, mechanical properties are generally assumed to remain constant despite external changes. However, factors such as temperature variations and changing external loads can influence these properties. These influences are referred to as thermoelasticity and non-linear elasticity, respectively. These changes become particularly significant when dealing with high-frequency elastodynamics. In such scenarios, the assumptions of linear elasticity no longer hold, and it becomes necessary to apply non-linear constitutive equations. Understanding and analyzing this non-linear behavior is essential for utilizing high-frequency elastic waves to detect changes in wave properties caused by temperature fluctuations or load variations in structural health monitoring and non-destructive testing.
Selected publications: To be updated
Research Interest 2: Body wave propagation in heterogeneous media
A material can exhibit both homogeneous and heterogeneous behavior simultaneously at different length scales, as seen in metals, rocks, and concrete. These heterogeneities can affect wave propagation, particularly for high-frequency waves, which are deflected by heterogeneities in a process known as scattering. Studying the behavior of scattered waves can provide insights into the meso-scale behavior of heterogeneous materials and help further explore the statistical properties of the heterogeneities within them.
Selected publications: To be updated
Research Interest 3: Material characterization using ballistic and diffuse waves
Ballistic waves, which travel in a straight line through the material and probe it as an effective medium, provide information about the macro-scale properties of the material, such as cracks and strain changes. In contrast, diffuse waves scatter within the material due to heterogeneities and have a broader spatial reach, offering insights into the meso-scale internal variations within a certain spatial range of the material. Analyzing the transport properties of these two distinct types of elastic waves provides a multi-scale tool for material characterization.
Selected publications: Paper 1
Research Interest 4: Application of wave interferomery in elastic wave-based structural health monitoring
Wave interferometry in seismology employs the cross-correlation of signal pairs to reconstruct the impulse response function, commonly known as the Green’s function, of the subsurface. This technique is also applicable to structural health monitoring, enabling the detection of changes within the medium. Wave interferometry techniques, whether implemented in the time or frequency domain, rely on cross-correlation or cross-spectrum to estimate travel time changes. Unlike traditional arrival-time pickers, such as those based on the Akaike Information Criterion (AIC), wave interferometry is more sensitive to subtle time shifts in the waveform. This heightened sensitivity allows for the detection of minute travel time changes in elastic waves, which can be caused by fluctuations in temperature or changes in stress.
Selected publications: Paper 1
Research Interest 5: Long-term monitoring of infrastructures
Long-term monitoring of infrastructures is a critical practice that ensures the safety, efficiency, and longevity of essential public assets such as bridges, roads, and tunnels. By continuously collecting and analyzing data on structural health, governments can detect potential failures before they become catastrophic. This proactive approach reduces maintenance costs, extends the lifespan of infrastructure, and safeguards public safety. For states and countries, it also supports economic stability, as reliable infrastructure is essential for transportation, trade, and emergency response. In the face of climate change and increasing urbanization, long-term monitoring has become a vital tool for sustainable development and disaster resilience. In the past few years, I participated in several long-term monitoring projects led by my supervisor, Assoc. Prof. Yuguang Yang. These projects include:
- Monitoring the Maastunnel in Rotterdam (see conference paper)
- Monitoring the N64 bridge near Dommelen, Valkenswaard
- Monitoring the geopolymer bridge in Friesland (see video 1 and video 2)
- Monitoring the Fehmarn Belt fixed link (together with Witteveen+Bos)
- Monitoring Concrete Bridge on Balladelaan in Amersfoort
Research Interest 6: Application of machine learning in civil engineering
The application of machine learning in civil engineering is transforming the way infrastructure is designed, constructed, and maintained. By leveraging advanced algorithms and data-driven models, engineers can predict structural performance, optimize construction processes, and enhance decision-making. From material testing to infrastructure monitoring, machine learning improves accuracy, efficiency, and sustainability, making it an essential tool for the future of civil engineering.