An Experimental and Theoretical Study of Pile Foundations Embedded in Sand Soil

This study aimed to examine the load carrying capacity of model instrumented piles embedded in sand soil, and to develop and verify reliable, highly efficient predictive models to fully correlate the non-linear relationship of pile load-settlement behaviour using a new, self-tuning artificial intelligence (AI) approach. In addition, a new methodology has been developed, in which the most effective pile bearing capacity design parameters can be precisely determined. To achieve this, a series of comprehensive experimental pile load tests were carried out on precast concrete piles, steel closed-ended piles and steel open-ended piles, comprised of three slenderness ratios of 12, 17 and 25, using an innovative calibrated testing rig, designed and manufactured at Liverpool John Moores University. The model piles were tested in a large pile testing chamber at a range of different densities of sand; loose (18%), medium (51%) and dense (83%). It is worth noting that novel structural fibres were utilised and optimised for different volume fractions to enhance the mechanical performance of concrete piles. The obtained results revealed that the higher the values of the of the pile effective length, Lc (embedded length of pile), sand density, and the soil-pile angle of shearing resistance, the higher the axial load magnitudes to reach the yield limit. This can be attributed to the increase in the end bearing point and mobilised shaft resistance. In addition, the plastic mechanism occurring in the surrounding soil was identified as the leading cause for the presence of nonlinearity in the pile-load tests. Furthermore, a new enhanced self-tuning supervised Levenberg-Marquardt (LM) training algorithm, based on a MATLAB environment, was introduced and applied in this process. The proposed algorithm was trained after conducting a comprehensive statistical analysis, the key objectives being to identify and yield reliable information from the most effective input parameters, highlight the relative importance “Beta values” and the statistical significance “Sig values” of each model input variable (IV) on the model output. To assess the accuracy and the efficiency of the employed algorithm, different measuring performance indicators (MPI), suggested in the open literature, were utilised. Common statistical performance indexes, i.e., root mean square error (RMSE), Pearson’s moment correlation coefficient (p), coefficient of determination (R), and mean square error (MSE) for each model were determined. Based on the graphical and numerical comparisons between the experimental and predicted load-settlement values, the results revealed that the optimum models of the LM training algorithm fully characterised load-settlement response with remarkable agreement. Additionally, the proposed algorithm successfully outperformed the conventional approaches, demonstrating the feasibility of the current study. New design charts have been developed to calculate the individual contribution of the most significant pile bearing capacity design parameters “the earth pressure coefficient (K) and the bearing capacity factor (N )”. The improved approach takes into account the change in sand relative density, pile material type, and the pile slenderness ratios. It is therefore a significant improvement over most conventional design methods recommended in the existing design procedures, which do not consider the influence of the most significant parameters that govern the pile bearing capacity design process

Spatial Interpolation of SPT with Artificial Neural Network

In large infrastructure projects, initial geotechnical investigation is conducted at large spacing (~ 100m to 250m), in which SPT is the common test performed while dynamic tests are limited in number. The preliminary planning and design of the buildings are performed based on this information. Hence, estimate of dynamic properties of soil (say, shear wave velocity) at building locations becomes necessary. This can be performed by estimation of SPT at building locations, by interpolation from borehole locations, and thereafter using correlation expressions for estimating shear wave velocity at building location. Interpolation of SPT has been handled earlier in literature with statistical and geospatial techniques. In this article, an artificial intelligence technique, namely, artificial neural network (ANN) is explored for addressing this problem. ANN allows multiple degrees of freedom to data and optimizes weights and biases of the network to yield the best possible estimates of the desired output, in this case, the SPT at intermediate locations. ANN is known to be robust in handling data with noise and thus would be suitable for this application. Five neighbouring points were found suitable for efficient and accurate spatial interpolation of SPT using ANN with two to three neurons in one hidden layer. The performance was very good (correlation higher than 0.9 and errors lower than 2) and better than the geo-statistical approaches reported in literature (correlation lower than 0.9 and errors higher than 6). Within the limits of the study, the number of degrees of freedom (varying from 9 to 37) of the ANN did not affect its generalization capability

CIVIL ENGINEERING, SCIENCE AND TECHNOLOGY CHALLENGES: GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING

The book is based on scientific and technological advances in various Geotechnical and Geoenvironmental Engineering areas of Civil Engineering. It nurtures therefore the exchange of discoveries among research workforces worldwide including those focusing on the vast variety of facets of the fundamentals and applications within the Geotechnical and Geoenvironmental Engineering area. To offer novel and rapid developments, this book contains original contributions covering theoretical, physical experimental, and/or field works that incite and promote new understandings while elevating advancement in the Geotechnical and Geoenvironmental Engineering fields. Works in closing the gap between the theories and applications, which are beneficial to both academicians and practicing engineers, are particularly of interest to this book that paves the intellectual route to navigate new areas and frontiers of scholarly studies in Geotechnical and Geoenvironmental Engineering area

On the wave propagation of the multi-scale hybrid nanocomposite doubly curved viscoelastic panel

In this paper, wave propagation analysis of multi-hybrid nanocomposite (MHC) reinforced doubly curved panel embedded in the viscoelastic foundation is carried out. Higher-order shear deformable theory (HSDT) is utilized to express the displacement kinematics. The rule of mixture and modified Halpin–Tsai model are engaged to provide the effective material constant of the MHC reinforced doubly curved panel. By employing Hamilton’s principle, the governing equations of the structure are derived and solved with the aid of an analytical method. Afterward, a parametric study is carried out to investigate the effects of the viscoelastic foundation, carbon nanotubes’ (CNTs’) weight fraction, various MHC patterns, radius to total thickness ratio, and carbon fibers angel on the phase velocity of the MHC reinforced doubly curved panel in the viscoelastic medium. The results show that, by considering the viscous parameter, the relation between wavenumber and phase velocity changes from exponential increase to logarithmic boost. A useful suggestion of this research is that the effects of fiber angel and damping parameter on the phase velocity of a doubly curved panel are hardly dependent on the wavenumber. The presented study outputs can be used in ultrasonic inspection techniques and structural health monitoring.publishe

Mechanical behavior of fibrous root-inspired anchorage systems

Plant root-inspired geotechnics seeks to harness the principles of one of Earth’s most ubiquitous foundation elements to redesign or enhance conventional geotechnical infrastructure. In particular, the anchorage and material efficiency attributes of fibrous root systems are encapsulated in a novel root-inspired anchor that has the capability of surpassing conventional anchorage systems (e.g. tiebacks, tiedowns, plate and pile anchors) particularly in areas with weak soil or spatial constraints. The scope of this research fully exposes the application of the bio-inspired design process to the realization of root-inspired anchorage systems from 1) the reasoning behind the selection of fibrous root systems as a prime source of inspiration for sustainable, resilient anchor elements (e.g. plastic and thigmotropic adaptability properties, multifunctionality), to 2) the identification of the critical attributes of fibrous root systems to pullout behavior through testing of leek (Allium porrum) and spider (Chlorophytum comosum) plants, to 3) the design and fabrication of root-inspired anchor models, to 4) an extensive performance evaluation. More specifically, the root-inspired anchors are assessed in terms of their pullout behavior through a combination of analytical, experimental, and numerical analyses. The slip line method from plasticity theory is used as the basis to derive a solution for the prediction of plate anchor pullout capacity that was further modified to account for the more complex geometry of root-inspired anchors through mechanics-informed insights. Experimentally, a series of 1g pullout tests are performed to parametrically study the role of root-inspired anchor features (i.e. morphology, topology, material properties, and interface roughness) as well as soil properties (i.e. relative density, particle angularity, and particle size) on pullout behavior. Additionally, through a combination of x-ray CT imaging and digital image correlation (DIC), the formation and evolution of the soil failure surface during the uplift of a root-inspired anchor model is visualized and analyzed to connect the local soil kinematics to the global pullout response. With the finite volume method, the uplift process is simulated to validate experimental results and to extend the parametric study to a wider range of anchor and soil conditions. Finally a few considerations are highlighted concerning the upscale design, installation, and testing of these next generation anchor elements.Ph.D

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

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems