In situ tests, such as the cone penetration test (CPT), are a popular tool for geotechnical engineers because they are an efficient and economical method for routine site characterisation, soil profiling and estimation of constitutive properties of soil. The main problem with interpreting in situ test results is the large amount of empiricism that engineers have to rely on. Furthermore, the rise in complexity of constitutive soil models have made soil interpretation from experimental data ever more challenging. Constitutive models in numerical analyses are used to simulate the stress-strain response of soils and each model is governed by a set of parameters that quantifies the mechanical behaviour of soil. A consequence for the increased complexity of a constitutive model is the increased number of parameters to be defined from a larger number of experimental tests. This is why parameter determination is still one of the most challenging tasks faced by geotechnical engineers. The challenges in soil interpretation from experimental data have led to an increasing demand for a more efficient parameter determination system for performing more reliable numerical simulations in geotechnical engineering. This study explores the applicability of graphs to develop a generic system for the determination of constitutive model parameters from in situ test results. Graphs are mathematical structures used to model pairwise relations between objects in a network and benefit from their ability to visualise complex problems. In the same manner, the use of graphs in a parameter determination system could generate valid relations, or paths, between parameters in a network. This should give the user of the system, i.e., the geotechnical engineer, both insight in and control over the system. The proposed strategy aims to increase the confidence of derived parameters from in situ test results. It should eventually provide guidance for the user in selecting the right constitutive model and corresponding parameters for the considered application, in order to use the full potential of numerical analysis. This study presents a proof of concept for an automated system to determine constitutive model parameters from in situ test results. Key aspects of the system are: transparency and adaptability. The system is kept transparent, since users are able to verify how available information (i.e., expertise by the engineer) is used by the system to arrive at a solution. The system is kept adaptable, since users can add their expertise into the system without having to make modifications to the system and since developers can easily expand the system in the future. This study illustrates how a system can automatically generate paths between parameters in a network (i.e., a graph),using the external database (e.g., a spreadsheet) as input by the geotechnical engineer. The focus of this system is on determining engineering parameters based on CPT data for coarse-grained soils. However, the universality of the system allows the system to be extended to a wider range of soils and in situ tests. Separate ongoing research efforts are devoted to further validating and tweaking of the system.

The main purpose of this thesis is to conclude whether terrestrial laser scanning (TLS) can be used as an automated method to improve the classification of soils. It was in collaboration with Fugro N.V., a large Dutch consultancy and engineering firm that is active in onshore and offshore services in the field of geological and geotechnical investigations for a wide field of clients in the petroleum and gas industry but also for other infrastructure. Current methods for the classification of soils comprise of laboratory and in-situ measurements. Fugro is searching for a more accurate and time efficient method to classify soil and provided five soil samples on which a classification had to be performed. The Leica C10 laser scanner, provided by the department of Geoscience and Remote Sensing (TU Delft), was used to scan the soil samples. TLS is based on LIDAR (light detection and ranging) which is known for emitting pulses in a very narrow beam of monochromatic light (one wavelength) in the ultraviolet, visible or near-infrared range of the electromagnetic spectrum. After scanning the soil samples, the following features extracted from the 3D point cloud data were used to perform classification on each soil sample: intensity (backscattered energy), colour (an RGB-image), surface height variations (roughness). The chosen method was iso cluster unsupervised classification. After classification, interpretations were made based on the descriptions of the soil sample and knowledge of bare soil reflectance. The main factors that influenced the soil reflectance were related to characteristics of the samples: glauconite content (an iron-bearing mineral) and surface roughness. Both factors were responsible for a decrease in soil reflectance. Unsupervised classification was applicable on three out of five samples. However, only one sample provided good results. The other two samples were less promising, although some features could clearly be identified after comparing the classified image with ground truth data.