368 research outputs found
Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model
Ground borne vibrations from high speed trains
A consequence of high speed rail transportation is the generation of
elevated ground borne vibrations. This thesis presents several original
contributions towards the prediction of these vibrations.
Firstly, a new three dimensional finite element model capable of
vibration prediction was developed. Its main feature was its ability to model
complex track geometries while doing so through a fully coupled vehicle-tracksoil
system. Model output was compared to experimental results obtained
during this thesis and also to independent data sets. It was shown to predict
velocity time histories, vibration frequency spectrums and international
vibration descriptors with high accuracy.
An appraisal of the suitability of a finite difference time domain
modelling approach for railway vibration prediction was also undertaken. This
resulted in the development of a new ‘higher order’ perfectly matched layers
absorbing boundary condition. This condition was found to offer higher
performance in comparison to current alternative absorbing boundary
conditions.
Field work was then undertaken on high speed lines with varying
embankment conditions in Belgium and England. Vibration data was recorded
up to 100m from each track and geophysical investigations were performed to
determine the underlying soil properties. The results were used for numerical
model validation and also to provide new insights into the effect of various
embankment conditions on vibration propagation. It was found that
embankments generate higher frequency excitation in comparison to nonembankment
cases and that cuttings generate higher vibration levels than noncuttings.
Once validated the finite element model was used to provide new
insights into the effect of train speed, embankment constituent materials and
railway track type on vibration levels. It was found that the shape and
magnitude of ground vibration increased rapidly as the train’s speed
approached the Rayleigh wave speed of the underlying soil. It was also found
that ballast, slab and metal tracks produced similar levels of vibration and that
stiffer embankments reduced vibration levels at distances near and far from the
track.
Two vibration mitigation techniques were also explored through
numerical simulation. Firstly, an analysis was undertaken to determine the
ability of a new modified ballast material to actively isolate vibration within the
track structure. Secondly, wave barrier geometries were investigated to
optimise their performance whilst minimising cost. It was found that barrier
depth was the most influential parameter, whereas width had little effect.
Additionally, geometry optimisation was found to result in a 95% cost saving in
comparison to a base case.
Using a vast array of results generated using the previously developed
finite element model, a new empirical prediction model was also developed,
capable of quickly assessing vibration levels across large sections of track.
Unlike currently available empirical models, it was able to account for soil
properties in its calculation and could predict a variety of international
vibration metrics. It was shown to offer increased prediction performance in
comparison to an alternative empirical model
GPR applications across Engineering and Geosciences disciplines in Italy: a review
In this paper, a review of the main ground-penetrating radar (GPR) applications, technologies, and methodologies used in Italy is given. The discussion has been organized in accordance with the field of application, and the use of this technology has been contextualized with cultural and territorial peculiarities, as well as with social, economic, and infrastructure requirements, which make the Italian territory a comprehensive large-scale study case to analyze. First, an overview on the use of GPR worldwide compared to its usage in Italy over the history is provided. Subsequently, the state of the art about the main GPR activities in Italy is deepened and divided according to the field of application. Notwithstanding a slight delay in delivering recognized literature studies with respect to other forefront countries, it has been shown how the Italian contribution is now aligned with the highest world standards of research and innovation in the field of GPR. Finally, possible research perspectives on the usage of GPR in Italy are briefly discussed
Dam Safety. Overtopping and Geostructural Risks
This reprintshows recent advances in dam safety related to overtopping and the prevention, detection, and risk assessment of geostructural risks. Related to overtopping, the issues treated are: the throughflow and failure process of rockfill dams; the protection of embankment dams against overtopping by means of a rockfill toe or wedge-shaped blocks; and the protection of concrete dams with highly convergent chutes. In the area of geostructural threats, the detection of anomalies in dam behavior from monitoring data using a combination of machine learning techniques, the numerical modeling of seismic behavior of concrete dams, and the determination of the impact area downstream of ski-jump spillways are also studied and discussed. In relation to risk assessment, three chapters deal with the development of fragility curves for dikes and dams in relation to various failure mechanisms
Artificial Neural Network Approaches For Slope Stability
Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2007Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2007Bu çalışmada 170 tane lokal bölgenin şev profili dataları kullanılarak yapay zeka mantığı yaklaşımlarından beş tane yapay sinir ağı mimarisi kullanılmıştır. Bunlar BPNN, geri yayılmalı sinir ağı mimarisi ve GRNN, genel regresyonlu yapay sinir ağı mimarisi, GMDH, gruplama methodu, Kohonen ve PNN, olasılık yöntemidir. Ancak sadece BPNN, geri yayılmalı sinir ağı mimarisi ve GRNN, genel regresyonlu yapay sinir ağı mimarisi model oluşturmakta kullanılmıştır. Bu yaklaşımlarda 9 adet girdi ve 1 tane çıkış parametreleri verilmiştir. Çıkış parametresi şev güvenlik katsayısı olup, girdi parametreleri şev yüksekliği ( H ), şev eğimi ( β ), yeraltı suyu derinliği ( Hw ), sağlam zemin derinliği ( Hb ), kohezyon ( c ), zemin içsel sürtünme açısı ( Φ ), kuru birim hacim ağırlığı ( γ ), düşey ve yatay sismik zemin katsayıları ( Kh , Kv )‘dır. Bu çalışmadaki amaç sismik zemin katsayılarının şev stabilitesindeki önemlerinin incelenmesidir. Sonuç olarak genel regresyon yapay sinir ağı modelinin daha başarılı olduğu ve % 92.5 başarı yüzdesine sahip olduğu görülmüş, düşey ve yatay sismik zemin katsayılarının şev yüksekliği, şev eğimi ve yeraltı suyu derinliğinden sonra şev stabilitesindeki etkisinin önemli olduğu görülmüştür.In this study 170 slope data and their properties are used by Artificial Intelligence approach five neural network approaches architecture These approaches are Back propagation neural network architecture ( BPNN ), General regression neural network ( GRNN ), Group method of data handling ( GMDH ), Kohonen learning paradigm and Probabilistic neural network ( PNN ) architectures. But only 2 of them used, these are the back propagation neural network architecture ( BPNN ) and the general regression neural network ( GRNN ). There are 9 input parameters and 1 output parameter. The output parameter is the factor of the safety of the slopes ( F.S. ), the input parameters are the height of slope ( H ), the inclination of slope ( β ), the height of water level ( Hw ), the depth of firm base ( Hb ), the cohesion of soil ( c ), the friction angle of soil ( Φ ), the unit weight of soil ( γ ), but the important input parameters are horizontal and vertical seismic coefficients ( kh , kv ).Trying to be obtained in this study is to see the importance of the seismic coefficients for a slope stability safety. In conclusion this study shows that general regression neural network (GRNN) approach is more useful model and have % 92.5 success rate for seeing the effect of earthquake for slope stability safety and generally horizontal and vertical seismic coefficients importance seen after the height of the slope ( H ), the inclination of slope ( β ), the height of water level (Hw) importance.Yüksek LisansM.Sc
Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures
In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools
Multiscale Regional Liquefaction Hazard Assessment and Mapping
Soil liquefaction is a major cause of damage during earthquakes that could trigger many kinds of ground failures such as ground settlement, lateral spreading, land slides, etc. These ground failures could cause damage to infrastructures such as buildings, bridges, and lifelines resulting in significant economic losses. Therefore it is of significant importance to assess liquefaction hazard. The triggering and consequencing ground failure of liquefaction have been well investigated in the past decades. Nowadays, the dominant approach that correlates the observed field behavior with various in-situ œindex tests is able to achieve considerably precise assessments for free field conditions at site-specific scale. Regional scale assessments of liquefaction hazard, however, are still underdeveloped. Issues such as cross-geologic units correlations are still not systematically investigated in regional liquefaction assessment. Therefore, the main objective of this dissertation is to develop a solution framework for reliable regional assessment of earthquake-induced liquefaction hazard. Another objective is to validate this framework by applying it to several earthquake-prone regions so that liquefaction hazard maps of these regions could be added to the literature and guide designers, engineers and researchers. Moreover, the dominant method of estimating liquefaction damages via empirical correlations are not capable for complex site conditions. Therefore another objective of this dissertation is to study alternative approaches for general estimation of liquefaction damages. To achieve these objectives, a multiscale modeling framework for better estimate of regional liquefaction hazard with material randomness and heterogeneity is developed. One advantage the developed methodology is the extension of conventional random field models to account for soil spatial variability at multiple scales and resolutions. The method allows selectively and adaptively generating random fields at smaller scales around critical areas or around areas where soil properties are known to a great detail from lab or field tests. The process is defined such that spatial correlation is consistent across length scales. Illustrative examples (Marina District in San Francisco, Alameda County in California, and Christchurch in New Zealand) are presented. Liquefaction hazard is evaluated at multi-scale. Compared with single scale analyses, multi-scale random fields provide more detailed information and higher-resolution soil properties around critical areas. This framework provides a new way to consistently incorporating small-scale local liquefaction analysis into large-scale liquefaction assessment mapping. Furthermore, finite element method is identified as a prominent alternative to traditional approach for liquefaction estimation via empirical correlations. A dynamic FEM model is built upon which an effective stress analysis is performed to estimate liquefaction-induced soil deformation at site-specific scale. It is shown the developed finite element model as a numerical tool can be used in predicting cyclic liquefaction in soils. This research is expected to shed light on the complete understanding of soil liquefaction during earthquakes in hoping of saving economic losses in the future
Aspectos técnico-científicos de barragens no Brasil: uma abordagem teórica
The safety of a dam is the result of a series of factors, including structural, geotechnical, hydraulic, operational and environmental aspects. In Brazil, Law No. 12.334 of September 2010 establishes the National Dam Safety Policy, which requires safety reports and monitoring inspections for existing dams. The inspection comprises a set of devices installed on the dam, which are used to assess the structural behavior based on performance parameters of the structure, such as displacements, flows, stresses, slopes and others. Dam auscultation procedures, historically, have been performed since the 1950s. Since then, there have been significant advances in instrumentation and dam auscultation methods. This work presents a theoretical approach on technical and scientific aspects of dams in Brazil, based on a state-of-the-art literature review, involving auscultation of dams in the context of design codes, concepts, instrumentation, safety, procedures and monitoring methods.A segurança de uma barragem é resultante de uma série de fatores, dentre os quais podem
ser citados aspectos estruturais, geotécnicos, hidráulicos, operacionais e ambientais. No Brasil,
a Lei nº 12.334 de setembro de 2010 estabelece a Política Nacional de Segurança de Barragens.
A instrumentação compõe um conjunto de dispositivos instalados nas barragens, que são
utilizados para avaliar o seu comportamento estrutural a partir de parâmetros de desempenho
da estrutura, tais como deslocamentos, vazões, tensões, inclinações e outros. Procedimentos de
auscultação de barragens, historicamente, tem sido realizado desde a década de 50, conforme a
literatura. Desde então, houve avanços significativos na instrumentação e nos métodos de
auscultação de barragens. Este trabalho tem como objetivo apresentar uma abordagem teórica sobre aspectos técnico-científicos de barragens no Brasil, fundamentada numa revisão de
literatura no estado da arte, envolvendo auscultação de barragens no contexto de normas,
conceitos, instrumentação, segurança, procedimentos e métodos de monitoramento.Uminho -Universidade do Minho(undefined
Vibration Isolation Using In-filled Geofoam Trench Barriers
A significant amount of numerical and experimental research has been conducted to study the vibration isolation by wave barriers considering open trenches, in-filled concrete or bentonite trenches, sheet-pile walls, and rows of piles. A few studies have investigated the use of expanded polystyrene (EPS) geofoam material as wave barriers, which indicated that in-filled geofoam trenches can be used as effective wave barriers. However, no engineering design method is available to date for the design of such type of wave barriers. This dissertation presents comprehensive experimental and numerical investigations on the use of in-filled geofoam trench barriers to scatter machine foundations vibration, in order to provide some recommendations and design guidelines for their implementation in design.
Two- and three-dimensional time-domain finite element models have been developed utilizing the finite element package ABAQUS. The numerical models have been verified and then used to study the effectiveness of different configurations of in-filled geofoam wave barriers. All the proposed configurations performed well in scattering surface waves. However, the single-continuous wall system was considered to be more economic and practical alternative for wave scattering.
Based on the findings of the preliminary numerical investigations, a full scale field experimental study has been conducted to investigate the performance of in-filled geofoam trenches. An innovative approach to construct geofoam trenches involving hydro-dig technology was utilized. A series of experimental tests have been conducted to evaluate the performance of both open and in-filled geofoam trench barriers considering their geometry and distance from the source of disturbance. The results of the field experimental investigations were analyzed and interpreted to provide recommendations for implementation in design. Experimental results confirmed that in-filled geofoam trench barriers can effectively reduce the transmitted vibrations and its protective effectiveness is comparable to the open trench barrier.
An extensive numerical parametric study was conducted to investigate the behaviour of in-filled geofoam wave barrier under different soil conditions and to point out the key parameters that dominate the performance of in-filled geofoam trench barriers. The influence of various key parameters on the screening performance were carefully analyzed and discussed. A model using Multiple Linear Regression (MLR) analysis was developed for design purpose. Finally, an artificial neural network (ANN) model has been developed, which aims at extrapolating the parametric study results to predict the in-filled geofoam wave barrier protective effectiveness in different soil profiles with different geometric dimensions
Advances in Sustainable River Management
The main objective of this Special Issue is to contribute in understanding and provide science-based knowledge, new ideas/approaches and solutions in sustainable river management, to improve water management policies and practices following different environmental requirements aspects
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