Application of an Artificial Neural Network for the CPT-based Soil Stratigraphy Classification

Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils. However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective and time-efficient approach, but more input parameters and data are needed for increasing its performance

Development of Soil Compressibility Prediction Models

The magnitude of the overall settlement depends on several variables such as the Compression Index, Cc, and Recompression Index, Cr, which are determined by a consolidation test; however, the test is time consuming and labor intensive. Correlations have been developed to approximate these compressibility indexes. In this study, a data driven approach has been employed in order to estimate Cc and Cr. Support Vector Machines classification is used to determine the number of distinct models to be developed. The statistical models are built through a forward selection stepwise regression procedure. Ten variables were used, including the moisture content (w), initial void ratio (eo), dry unit weight (γdry), wet unit weight (γwet), automatic hammer SPT blow count (N), overburden stress (σ), fines content (-200), liquid limit (LL), plasticity index (PI), and specific gravity (Gs). The results confirm the need for separate models for three out of four soil types, these being Coarse Grained, Fine Grained, and Organic Peat. The models for each classification have varying degrees of accuracy. The correlations were tested through a series of field tests, settlement analysis, and comparison to known site settlement. The first analysis incorporates developed correlations for Cr, and the second utilizes measured Cc and Cr for each soil layer. The predicted settlements from these two analyses were compared to the measured settlement taken in close proximity. Upon conclusion of the analyses, the results indicate that settlement predictions applying a rule of thumb equating Cc to Cr, accounting for elastic settlement, and using a conventional influence zone of settlement, compares more favorably to measured settlement than that of predictions using measured compressibility index(s). Accuracy of settlement predictions is contingent on a thorough field investigation

Prediction of the Effectiveness of Rolling Dynamic Compaction Using Artificial Intelligence Techniques and In Situ Soil Test Data

The research presented in this thesis focuses on developing predictive tools to forecast the effectiveness of rolling dynamic compaction (RDC) in different ground conditions. Among many other soil compaction methods, RDC is a widespread technique, which involves impacting the ground with a heavy (6–12 tonnes) non-circular (3-, 4- and 5- sided) module. It provides the construction industry with an improved ground compaction capability, especially with respect to a greater influence depth and a higher speed of compaction, resulting in increased productivity when compared with traditional compaction equipment. However, to date, no rational means are available for obtaining a priori estimation of the degree of densification or the extent of the influence depth by RDC in different ground conditions. In addressing this knowledge gap, the research presented in this thesis develops robust predictive models to forecast the performance of RDC by means of the artificial intelligence (AI) techniques in the form of artificial neural networks (ANNs) and linear genetic programming (LGP), which have already been proven to be successful in a wide variety of forecasting applications in geotechnical engineering aspects. This study is focussed solely on the 4-sided, 8 tonne impact roller (BH-1300) and the AI-based models incorporate comprehensive databases consisting of in situ soil test data; specifically cone penetration test (CPT) and dynamic cone penetration (DCP) test data obtained from many ground improvement projects involving RDC. Thus, altogether, two distinct sets of optimal models: two involving ANNs – one for the CPT and the other for the DCP; and two LGP models – again, one for the CPT and the other for the DCP – are presented. The accuracy and the reliability of the optimal model predictions are assessed by subjecting them to various performance measures. Furthermore, each of the selected optimal models are examined in a parametric study, by which the generalisation ability and the robustness of the models are confirmed. In addition, the performance of the optimal ANN and LGP-based models, as well as other aspects, are compared with each other in order to assess the suitability and shortcomings of each. Consequently, a recommendation has been made of the most feasible approach for predicting the effectiveness of RDC in different ground conditions with respect to CPT and DCP test data. The models have also been disseminated via a series of mathematical formulae and/or programming code to facilitate their application in practice. It is demonstrated that the developed optimal models are accurate and reliable over a range of soil types, and thus, have been recommended with confidence. As such, the developed models provide preliminary estimates of the density improvement in the ground based on the subsurface conditions and the number of roller passes. Therefore, it is considered that the models are beneficial during the pre-planning stages, and may replace, or at the very least augment, the necessity for RDC field trials prior to fullscale construction. In addition, the analyses demonstrate that the AI techniques provide a feasible approach for non-linear modelling involving many parameters, which in turn, further encourages future applications in the broader geotechnical engineering context. Finally, a comprehensive set of guidelines for each of the AI techniques employed in this research, i.e. ANN and LGP, is provided, with the intention of assisting potential and current users of these techniques.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

Cone Penetration Testing 2022

This volume contains the proceedings of the 5th International Symposium on Cone Penetration Testing (CPT’22), held in Bologna, Italy, 8-10 June 2022. More than 500 authors - academics, researchers, practitioners and manufacturers – contributed to the peer-reviewed papers included in this book, which includes three keynote lectures, four invited lectures and 169 technical papers. The contributions provide a full picture of the current knowledge and major trends in CPT research and development, with respect to innovations in instrumentation, latest advances in data interpretation, and emerging fields of CPT application. The paper topics encompass three well-established topic categories typically addressed in CPT events: - Equipment and Procedures - Data Interpretation - Applications. Emphasis is placed on the use of statistical approaches and innovative numerical strategies for CPT data interpretation, liquefaction studies, application of CPT to offshore engineering, comparative studies between CPT and other in-situ tests. Cone Penetration Testing 2022 contains a wealth of information that could be useful for researchers, practitioners and all those working in the broad and dynamic field of cone penetration testing

Approximate Reasoning in Hydrogeological Modeling

The accurate determination of hydraulic conductivity is an important element of successful groundwater flow and transport modeling. However, the exhaustive measurement of this hydrogeological parameter is quite costly and, as a result, unrealistic. Alternatively, relationships between hydraulic conductivity and other hydrogeological variables less costly to measure have been used to estimate this crucial variable whenever needed. Until this point, however, the majority of these relationships have been assumed to be crisp and precise, contrary to what intuition dictates. The research presented herein addresses the imprecision inherent in hydraulic conductivity estimation, framing this process in a fuzzy logic framework. Because traditional hydrogeological practices are not suited to handle fuzzy data, various approaches to incorporating fuzzy data at different steps in the groundwater modeling process have been previously developed. Such approaches have been both redundant and contrary at times, including multiple approaches proposed for both fuzzy kriging and groundwater modeling. This research proposes a consistent rubric for the handling of fuzzy data throughout the entire groundwater modeling process. This entails the estimation of fuzzy data from alternative hydrogeological parameters, the sampling of realizations from fuzzy hydraulic conductivity data, including, most importantly, the appropriate aggregation of expert-provided fuzzy hydraulic conductivity estimates with traditionally-derived hydraulic conductivity measurements, and utilization of this information in the numerical simulation of groundwater flow and transport

Automated Soil Classification And Identification Using Machine Vision And Artificial Neural Networks

DissertationIntroduction: In the construction industry, one of the major considerations when designing a superstructure is which foundation should be selected. Foundations provide support to superstructures by transferring the load of the structure evenly into the earth. An inappropriate foundation choice could result in damage to the superstructure, or even the collapse of such a structure. The clay content of soil is a major determining factor when selecting a foundation type. Soil containing clay, has the potential to shrink and swell as the water content changes. This heaving of the soil can cause damage to the superstructure built upon it. Determining the amount of clay in a soil sample, is one of the most important steps in the soil classification process. In South Africa, the Hydrometer method is commonly used to determine the clay content of soil samples. This method is a manual, time intensive soil classification method, with doubtful accuracy. This study was undertaken to develop an Automated Soil Classification System (ASCS) that will classify soil more accurately and more expeditiously, making it cost and time effective. This was achieved by applying a Machine Vision (MV) process to soil samples, to generate unique digital soil sample fingerprints for soil samples. This process was then combined with an Artificial Neural Network (ANN), to automatically classify the soil sample from the fingerprints. Methods: Initially a Machine Vision Instrument (MVI) was constructed for the consistent capturing of high fidelity images during the sedimentation process of a soil sample. Software was then developed to process these captured images and generate unique Soil Sample (SS) fingerprints for different soil constitutions. Four investigations were preformed to validate the consistency of the SS fingerprints generated with the MVI. These investigations were: 1. Validation of the SS fingerprint generation process; 2. Validation of the soil sample preparation procedure; 3. Determination of the differentiation ability of the MVI; and 4. Validation of the MVI by generating SS fingerprints for coded (unknown) soil samples. The generated SS fingerprints were then used to train an ANN to recognise and classify soil samples from their respective SS fingerprints. After the training of the ANN, a fifth investigation was undertaken determine the accuracy of the trained ANN and a final, sixth investigation was undertaken to compare the performance of the ASCS to that of the Hydrometer method. Results: The constructed MVI was able to acquire good quality greyscale images during the sedimentation process of soil samples in a consistent manner. Investigations 1 through 4 showed that correlation amongst SS fingerprints, generated from the same soil sample, was in the order of 97%, while the correlation amongst SS fingerprints, generated from multiple soils samples of the same constitution, was in the order of 95%. Investigation five showed that the training of the ANN was successful as the R values obtained after training were greater than 0,98. The sixth and final investigation showed that the accuracy of the ASCS was in the range of 95% and greatly outperformed the Hydrometer method, who’s accuracy varied from approximately 49 to 89%. The ASCS also delivered these results in 28 hours while the Hydrometer method took approximately seven days

Development of Next Generation Liquefaction (NGL) Database for Liquefaction-Induced Lateral Spread

TPF-5(350)This report presents several advancements in the empirical modeling of liquefaction-induced lateral spread. It starts with a newly collected dataset of 5,560 historical lateral spread displacement vectors, a sample size over 10 times larger than the existing databases and subsurface data comprising over 633 standard penetration test boreholes. This work presents a comprehensive comparison of state-of-the-art empirical models for lateral spreads through Monte Carlo simulations and sensitivity analyses and proposes new evaluation metrics to measure performance. It also quantifies the uncertainty of model weights of the Multiple Linear Regression (MLR) model using Bayesian Statistics. A new functional form is proposed for the MLR model using the least absolute shrinkage and selection operator method. Importantly, the conventional probabilistic framework for predicting lateral spread is expanded to account for the probability of lateral spread triggering given the triggering of liquefaction. This expansion allows us to model zero-displacement lateral spreads despite having liquefaction susceptibility. A convolutional neural network classifier is developed to model the probability of lateral spread triggering with an out-of-fold model accuracy of 90.5%. A new mathematical representation of soil types is presented and trained in the context of liquefaction and lateral spread and boosted model performance

Modelling of a generalized thermal conductivity for granular multiphase geomaterial design purposes

Soil thermal conductivity has an important role in geo-energy applications such as high voltage buried power cables, oil and gas pipelines, shallow geo-energy storage systems and heat transfer modelling. Hence, improvement of thermal conductivity of geomaterials is important in many engineering applications. In this thesis, an extensive experimental investigation was performed to enhance the thermal conductivity of geomaterials by modifying particle size distribution into fuller curve gradation, and by adding fine particles in an appropriate ratio as fillers. A significant improvement in the thermal conductivity was achieved with the newly developed geomaterials. An adaptive model based on artificial neural networks (ANNs) was developed to generalize the different conditions and soil types for estimating the thermal conductivity of geomaterials. After a corresponding training phase of the model based on the experimental data, the ANN model was able to predict the thermal conductivity of the independent experimental data very well. In perspective, the model can be supplemented with data of further soil types and conditions, so that a comprehensive representation of the saturation-dependent thermal conductivity of any materials can be prepared. The numerical 'black box' model developed in this way can generalize the relationships between different materials for later added amounts of data and soil types. In addition to the model development, a detailed validation was carried out using different geomaterials and boundary conditions to reinforce the applicability and superiority of the prediction models