Structural reliability methods are often used to evaluate the failure performance of geotechnical structures. A common approach is to use the first-order reliability method. Its popularity results ...from the mathematical simplicity of the method, since only second moment information (mean and coefficient of variation) on the random variables is required. The probability of failure is then assessed by an index known commonly as the reliability index. One critical aspect in determining the reliability index is the explicit definition of the limit state surface of the system. In a problem involving multi-dimensional random variables, the limit state surface is the boundary separating the safe domain from the "failure" (or lack of serviceability) domain. In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite element method, this surface may be difficult to determine explicitly in terms of the random variables, and therefore the limit state can only be expressed implicitly rather than in a closed-form solution. It is proposed in this paper to use an artificial intelligence technique known as the back-propagation neural network algorithm to model the limit state surface. First, the failure domain is found through repeated point-by-point numerical analyses with different input values. The neural network is then trained on this set of data. Using the optimal weights of the neural network connections, it is possible to develop a mathematical expression relating the input and output variables that approximates the limit state surface. Some examples are given to illustrate the application and accuracy of the proposed approach.Key words: first-order reliability method, geotechnical structures, limit state surface, neural networks, reliability.
Le comportement mécanique des sols granulaires est un élément important à prendre en compte dans l'ingénierie géotechnique. Les approches de modélisation actuelles pour le comportement des sols ...granulaires utilisent des relations constitutives phénoménologiques basées sur la mécanique classique du continuum. Ce problème peut être contourné en utilisant des relations constitutives multi-échelles basées sur les principes thermodynamiques avec variables internes. En utilisant une approche multi-échelle, cette thèse tente de construire des relations constitutives multi-échelles qui tiennent compte de la microstructure des sols granulaires et les mettre en oeuvre pour résoudre des problèmes géotechniques à la fois en petites et grandes déformations. La thèse vise à: 1) construire une relation constitutive multiéchelle pour les sols granulaires secs à partir d'un cadre thermodynamique qui nécessite moins d'hypothèses ad hoc; 2) étendre les formulations thermomécaniques multi-échelles aux sols granulaires partiellement saturés pour lesquels un modèle micromécanique est formulé; 3) implémenter le modèle en utilisant un algorithme d'intégration implicite dans un code aux éléments finis; 4) appliquer le modèle pour analyser l'instabilité des sols granulaires dans les cas de ruptures localisées et diffuses; et 5) démontrer la capacité de l'approche multi-échelle à résoudre certains problèmes géotechniques typiques en mettant en oeuvre le modèle dans un code aux éléments finis explicite. L'approche multi-échelle proposée aboutit à un outil de simulation qui fournit des informations précieuses sur les problèmes d'ingénierie depuis l'échelle des grains jusqu’à l’échelle de la structure.
The mechanical behaviour of granular soils is an important aspect in geotechnical engineering. Current modelling approaches for the behaviour of granular soils employ phenomenological constitutive relations based upon classical continuum mechanics. This problem can be circumvented by using multiscale constitutive relations based on thermodynamic principles with internal variables. Using a multiscale approach, this thesis attempts to construct multiscale constitutive relations that account for the microstructure of granular soilsand to demonstrate their capabilities in solving geotechnical problems at both small and large deformations. The thesis aims to: 1) construct a multiscale constitutive relation for dry granular soils based on a thermodynamic framework which requires fewer ad hoc assumptions; 2) extend the multiscale thermomechanical formulations for partially saturated granularsoils for which a micromechanical model is formulated; 3)implement the model using an implicit integration algorithm in a finite element code; 4) apply the model to analyse the instability of granular soils for both localised and diffuse failures; and 5) demonstrate the capability of the multiscale approach in solving some typical geotechnical problems by implementing the model in an explicit finite element code. The proposed multiscale approach offers a simulation tool that provides valuable insights into engineering problems from the grain to the structure scale.
Deformations in mining areas are of key importance for human safety and the use of infrastructure. The surface observation of the scale of movements allows for the assessment of the correctness of ...mining subsidence modeling and forecasting. The study area is located above the underground mining. Levelling measurements were made to observe subsidence. The obtained results indicated anomalies in the terrain deformation. This hypothesis was verified using the PSInSAR technique. At the same time, the analysis carried out allowed for the detection of 3 regions forming subsidence troughs. Additionally, the observations made it possible to verify the stability of the levelling points. These results will allow for better management in planning subsequent measurement campaigns.
This paper presents region-based distress classification of road infrastructures via convolutional neural networks (CNN) without region annotation. Although CNNs are often used for classification ...tasks recently, CNNs trained from images which contain unnecessary regions cannot perform precise classification. Distress images of road infrastructures contain various unnecessary objects other than the target distress. Although target regions should be provided in order to achieve high performance, it is a time-confusing task for engineers. This paper focuses on removing unnecessary objects in the images without region annotation via an object detection method. Especially, by using a pre-trained object detection model with distress images of road infrastructures, distress regions in the images are detected automatically. Our proposed CNN trained from the obtained distress regions realizes precise distress classification.
Accurate assessment of the internal stability against suffusion for granular soils is very essential in order to prevent geotechnical structures failure. Many methods are available for this purpose ...with variety degrees of accuracy and usage complicity. This study is presenting a simple and a reliable method to assess the internal stability. The delimiting particle size of the granular soil is computed based on graphical method, and then the controlling constriction size, D c35 , of the primary soil skeleton is obtained based on an empirical equation and compared with the diameter of fine particles, d 85 . The soil is considered as internally stable if it has D c35 ⁄d 85 <0.75. The assessment of new method was compared with the most frequently used ones, as well as experimental tests, and its assessment was found to be fairly good.
Presents a collection of slides covering the following topics: enterprise asset management program; MTA bridges; MTA tunnels; ISO 55001; organizational arrangements; and governance framework.
Highways England operates and maintains England's Strategic Road Network (SRN). The continued function and overall resilience of the SRN is key to UK economy and trade. Geotechnical assets have been ...resilient to severe weather over recent decades. However, without timely maintenance, specific assets may incur increased deterioration rates. Climate change projections, which suggest changes in the frequency and magnitude of severe weather, could exacerbate asset deterioration. This paper presents a resilience management framework which will enable Highways England to plan ahead to adapt, prioritise, recover and intervene on those geotechnical assets vulnerable to severe weather events before performance and serviceability issues manifest. The context of this study across other asset groups within the organisation is also briefly explored.
In order to solve the problem of evaluation error in traditional road performance evaluation model, the optimization design of asphalt mixture pavement performance evaluation model is realized by ...using data mining technology. Design and set up evaluation standards for the use of asphalt pavement, and use data mining technology in the network environment to obtain relevant data on asphalt mixture road performance, and use this as the input of the model. The asphalt mixture is classified, and the corresponding influencing factors are analyzed for different types, combined with the asphalt mixture type and influencing factors, the road performance evaluation index is selected, and the corresponding weight value is calculated. Combined with the input value of the model and the test results of asphalt mixture pavement performance, the comprehensive road performance evaluation data is obtained, and the final evaluation result of asphalt mixture pavement performance is obtained by comparing with the evaluation standard. Compared with the traditional evaluation model, the error is reduced by 27.4 points.
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