Comprehensive site investigation of an offshore landfill using multi-geophysical methods

• Main Author: Zhang, Zhibo
• Other Authors: Fei Xunchang
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Earth and Environmental Sciences; Engineering; Geophysical investigation; Landfill; Machine learning; Incineration bottom ash; Cyclic shear; Municipal solid waste; ERT; IP; MASW
• Online Access: https://hdl.handle.net/10356/182458
• Institution: Nanyang Technological University

With the rapid increase in global waste generation, waste management has become an urgent global issue. This thesis begins with a scientometric analysis of solid waste research (SWR) trends, examining over 17,000 related publications to explore the evolving trends and influencing factors. The current focus of SWR is shifting towards biological treatment, waste source reduction, waste recycling, and the properties and reutilization of byproducts. Despite the growing interest in emerging technologies like “biogas”, established methods including “landfilling” and “incineration”, handle over 80% of the world's waste disposal needs. Given Singapore's imperative need to extend the lifespan of its only operational landfill, landfill mining and waste reuse present viable solutions. Therefore, a thorough investigation of different types of waste under the unique conditions of the offshore landfill is essential and fundamental to all subsequent efforts. This thesis explores the application of machine learning-enhanced multi-geophysical site investigation methods at Semakau Landfill (SL). The study employs Electrical Resistivity Tomography (ERT), Induced Polarization (IP), Multichannel Analysis of Surface Waves (MASW), and Microtremor Array Measurement (MAM) to investigate waste layers in SL, validated by geotechnical and geochemical test results. To accurately describe the heterogeneity of the landfill, unsupervised machine learning clustering methods are used to stratify the wastes based on geophysical investigation results, providing explicit classification criteria. This approach performs well in long-term cells, yielding a 3D stratified model consistent with borehole records. Geophysical results indicate that wastes with a high proportion of unincinerated materials have higher resistivity and chargeability than marine clay, which can be used to accurately determine the waste boundary. Low resistivity usually indicates higher saturation or concentrated leachate. High polarization areas may contain more metallic waste or active biodegradation reactions. Shear wave velocity (Vs) can be used to distinguish embankment boundaries and assess the consolidation degree of wastes. Despite the significant variability in the properties of mixed waste materials (MM), the trend of averaged geophysical parameters across different depth levels can be used to reasonably estimate waste depth, water table, and the distribution of stiff layers. The relationship between Vs and Standard Penetration Test (SPT) obtained through regression analysis allows for reasonable predictions of the stiffness of MM based on surface wave survey results. Using SPT and Igeo (Geochemical index) as labels, training geophysical results with the Artificial neural network (ANN) method provides reliable predictions of SPT and Igeo for areas without drilling and sampling. This method, combined with a set of geotechnical and geochemical data, enables a reasonable estimation and description of the stiffness and contamination levels across the entire area. To verify the feasibility of reusing waste materials, this thesis employs cyclic simple shear tests and shear wave velocity measurements to analyze the static and dynamic shear properties of incinerator bottom ash (IBA). The friction angle of IBA varies from 38.3 to 42.5° as density increases, while it consistently exhibits strain-hardening behavior. IBA tends to liquefy under unidirectional cyclic test conditions with lower cyclic stress ratio (CSR) and consolidation stress, while cyclic mobility failure is more common for multi-directional cyclic tests with higher CSR and consolidation stress. The cyclic resistance ratio reduction factor (RFCRR) for IBA ranges from 0.70 to 0.73, which can be used to correct the overestimation of cyclic shear resistance obtained from unidirectional cyclic shear tests. The cyclic stress ratio – normalized shear wave velocity (CSR – Vs1) plot shows that the liquefaction behavior of IBA under cyclic shear loading is similar to that of natural sand with 5% fine content, suggesting that fine content might be a factor controlling the liquefaction behavior of IBA.