António Gomes Correia

New Models for Strength and Deformability Parameter Calculation in Rock Masses Using Data-Mining Techniques

Due to the inherent geological complexity and characterization difficulties in rock formations, the evaluation of geomechanical parameters is very complex, mostly in the initial project stages and in small-scale geotechnical works, where information is scarce for the definition of an accurate geotechnical model. However, in large geotechnical projects, a great amount of data are produced and used to establish near-homogeneous geotechnical zones. If properly analyzed, these data can provide valuable information that can be used in situations where knowledge of the rock mass is limited. Yet, this implies the organization of geotechnical data in formats for proper analysis using advanced tools which is not normally done. Data-mining techniques have been successfully used in many fields but scarcely in geotechnics. They seem to be adequate as an advanced technique for analyzing large and complex databases that can be built with geotechnical information within the framework of an overall process of knowledge d...

A data mining approach for Jet Grouting Uniaxial Compressive Strength prediction

Jet Grouting (JG) is a Geotechnical Engineering technique that is characterized by a great versatility, being the best solution for several soil treatment improvement problems. However, JG lacks design rules and quality control. As the result, the main JG works are planned from empirical rules that are often too conservative. The development of rational models to simulate the effect of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this work, three data mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were adapted to predict the Uniaxial Compressive Strength (UCS) of JG laboratory formulations. A comparative study was held, by using a dataset used that was obtained from several studies previously accomplished in University of Minho. We show that the novel data-driven models are able to learn with high accuracy the complex relationships between the UCS of JG laboratory formulations and its contributing factors.

Determination of the Small-Strain Stiffness of Hard Soils by Means of Bender Elements and Accelerometers

AbstractDirect determination of seismic wave velocities in the laboratory is becoming common practice worldwide, given its great potential in the definition of the stiffness at very small strains. One of the techniques for seismic wave measurement makes use of piezoelectric transducers, such as bender elements (BE). However, some limitations remain to the applicability of this technique, namely for stiff geomaterials, such as compacted soils, naturally or artificially cemented soils and soft or weak rocks. To overcome this issue, two accelerometers have been used in conjunction with BE. In the present paper, this combined test setup implemented on a stress-path triaxial chamber will be detailed. An application study will be presented for a hard soil, prepared by laboratory compaction and tested in triaxial compression at different isotropic stress levels. The equipments, procedures and interpretation analyses will also be described. The advantages of this setup are twofold: (1) the interpretation of the acceleration measurements is straightforward, since the signals are of the same nature; (2) these measurements can be used to verify the BE signals, and thus minimize the subjectivity of the interpretation of BE results. Additionally, the accelerometers can be used autonomously wherever the interpretation of BE becomes too complex. The results of this research enabled to validate the interpretation methods used for BE testing. Moreover, this combined setup of transducers provided a simple yet powerful tool for eliminating the subjectivity inherent to BE testing, enabling reliable measurements of small-strain stiffness for a wide range of materials.

Using data mining techniques to predict deformability properties of jet grouting laboratory formulations over time

Jet Grouting (JG) technology is one of the most used softsoil improvements methods. When compared with other methods, JG is more versatile, since it can be applied to several soil types (ranging from coarse to fine-grained soils) and create elements with different geometric shapes (e.g. columns, panels). In geotechnical works where the serviceability limit state design criteria is required, deformability properties of the improved soil need to be quantified. However, due to the heterogeneity of the soils and the high number of variables involved in the JG process, such design is a very complex and hard task. Thus, in order to achieve a more rational design of JG technology, this paper proposes and compares three data mining techniques in order to estimate the different moduli that can be defined in an unconfined compressed test of JG Laboratory Formulations (JGLF). In particular, we analyze and discuss the predictive capabilities of Artificial Neural Networks, Support Vector Machines or Functional Networks. Furthermore, the key parameters in modulus estimation are identified by performing a 1-D sensitivity analysis procedure. We also analyze the effect of such variables in JGLF behavior.

Contribution of Loamy Soil Treatment to Improve Embankments Performance

This paper shows, for a loamy soil, the benefits of lime treatment on the evolution of the geomechanical properties governing the slope stability and the resistance against erosion of embankments. The main results indicate that the effective internal friction angle (φ’) is quite unchanged through time, while the effective cohesion (c’) strongly increases. Furthermore, erosion tests using the LCPC erodimeter on slopes between 0 and 30 degrees, and for a curing time up to 112 days, have shown that the treated soil becomes insensitive to erosion after a few days. These results clearly establish the benefits of lime treatment in safety and serviceability of embankments for highways and high speed trains and, consequently, have an important impact on the reduction of their life cycle costs.

Design and construction of pavements and rail tracks : geotechnical aspects and processed materials

Design and Construction of Pavements and Rail Tracks - Geotechnical Aspects and Processed Materials is a compilation of selected contributions produced between 2002 and 2005 by the International Committee TC3 - Geotechnics of Pavements of the International Society of Soil Mechanics and Geotechnical Engineering (ISSMGE), a committee dedicated to gathering current knowledge of geotechnical aspects relating to pavements and rail tracks. The volume presents advanced procedures for laboratory and field materials characterization, including processed materials (non-conventional road construction materials), novel tests for field stiffness evaluation, a pre-standard for roller integrated continuous compaction control and new theories for evaluation of the long term performance of materials, including environmental aspects. These contributions represent the latest developments relating to the design, construction and long term performance of pavements, rail tracks and earth structures, with emphasis on the geotechnical and environmental background.

Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway

National Road Network which consists of a traditional road structure and modern roads, require planned maintenance and should be in accordance with the needs. The limited choice of available national road network and the deviation of the overloading encourage the government to be more responsive to carry out maintenance management. The institution in charge of road maintenance is often constrained by the limited budget available. A two-objective optimization model considers maximum roughness and minimum maintenance cost for used road network with overload. The study was conducted on the entire national road network in West Java which are paved with flexible pavement. In the proposed approach, data mining model are used for predicting the roughness index over a given period of time. Routine and periodic maintenance are chosen in this study. Multi-objective optimization model was developed based on Genetic Algorithms. Budget constraints and overloading are the two constraints in the developed model. Based on the R-Tools result, the Pareto optimal solutions of the two objective functions are obtained. From the optimal solutions represented by roughness index and cost, an agency more easily obtain the information of the maintenance planning. Results of the developed model has been implemented through the selection of maintenance on the road network scenarios with different levels of overload.

Combining Data Mining and Evolutionary Computation for Multi-criteria Optimization of Earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Prediction of Soil-Cement Mechanical Properties Using EC2 Approach

For a better design of Jet Grouting (JG) and Cutter Soil Mixing (CSM) technologies, a set of laboratory formulations are usually prepared aiming to give a first idea of the mechanical behavior of the final mixture. However, these formulations can represent an important cost to the project. Therefore, aiming to reduce such cost, in the present work the analytical expressions proposed by Eurocode 2 for strength and stiffness prediction of concrete were adapted to soil-cement laboratory formulations for JG and CSM projects. It is shown that these expressions can be successful applied in mechanical properties prediction over time of soft soil stabilized with cement for a wide range of cement content, water cement ratios and soil types.

Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time

Sometimes, the soil foundation is inadequate for constructions purpose (soft-soils). In these cases there is need to improve its mechanical and physical properties. For this purpose, there are several geotechnical techniques where Jet Grouting (JG) is highlighted. In many geotechnical structures, advance design incorporates the ultimate limit state (ULS) and the serviceability limit state (SLS) design criteria, for which uniaxial compressive strength and deformability properties of the improved soils are needed. In this paper, three Data Mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were used to estimate the tangent elastic Young modulus at 50% of the maximum stress applied (E tg50%) of JG laboratory formulations over time. A sensitivity analysis procedure was also applied in order to understand the influence of each parameter in E tg50% estimation. It is shown that the data driven model is able to learn the complex relationship between E tg50% and its contributing factors. The obtained results, namely the relative importance of each parameter, were compared with the predictive models of elastic Young modulus at very small strain (E 0) as well as the uniaxial compressive strength (Q u ). The obtained results can help to understand the behavior of soil-cement mixtures over time and reduce the costs with laboratory formulations.