Multivariate Analysis to Relate CTOD Values with Material Properties in Steel Welded Joints for the Offshore Wind Power Industry

[EN] The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited parameters for modeling and control of crack growth, and it is broadly used at the industrial level. We have modeled, through multivariate analysis techniques, the relationships among CTOD values and other material properties (such as hardness, chemical composition, toughness, and microstructural morphology) in high-thickness offshore steel welded joints. In order to create this model, hundreds of tests were done on 72 real samples, which were welded with a wide range of real industrial parameters. The obtained results were processed and evaluated with different multivariate techniques, and we established the significance of all the chosen explanatory variables and the good predictive capability of the CTOD tests within the limits of the experimental variation. By establishing the use of this model, significant savings can be achieved in the manufacturing of wind generators, as CTOD tests are more expensive and complex than the proposed alternatives. Additionally, this model allows for some technical conclusions.S

Sustainable earthworks: Optimization with the ICOM method

Tmrees, EURACA, 13 to 16 April 2020, Athens, Greece[EN] In the construction of highways and roads, one of the main activities is earthworks. This activity has an economic and environmental impact that cannot be overlooked. The classic method, based on the use of mass diagram models and optimization, does not take into account the type and quality of the material found on site, making it difficult to optimize the actual flow of each material. The ICOM method (Intelligent Method of Optimized Mass Compensation) allows the optimization of classic works such as excavations and fillings resulting in the optimization of operating costs. This versatile method contemplates different options for each project and allows choosing the most appropriate one taking into account, among other factors, the distance travelled by each type of material, which translates into the amount of CO2 emitted and waste generated. This is why the use of the iCom method will enable us to make the work sustainable, while reducing environmental pollution and the amount of waste. This article compares the results obtained by applying the ICOM method with those that can be obtained with the classic method for twenty-four work projects in Spain and Portugal. The results analysed show that the ICOM method achieves a significant reduction in financial costs between 5% and 14.1% and a shortening of the time needed to carry out the work. The method also obtains a reduction in CO2 emissions (between 5.1% and 14%), while generating a smaller volume of waste materials, which implies a reduction in environmental impact. Furthermore, this method provides the reports, plans and diagrams necessary for the complete definition of the earthworks to be carried outS

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

[EN] The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.S

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems

Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty

Anomaly shape inversion via model reduction and PSO

Most of the geophysical inverse problems in geophysical exploration consist in detecting, locating and outlining the shape of geophysical anomalous bodies imbedded into a quasi- homogeneous background by analyzing their effect in the geophysical signature. The usual algorithm creates a very fine mesh in the model space to approximate the shapes and the values of the anomalous bodies and the geophysical structure of the geological background. This approach results in discrete inverse problems with a huge uncertainty space, and the common way of stabilizing the inversion consists in introducing a reference model (through prior information) to define the set of correctness of geophysical models. This method has some drawbacks if the reference model is incorrect, leading to a wrong inverse solution. We present a different way of dealing with the high underdetermined character of this kind of problems, consisting in solving the inverse problem using a low dimensional parameterization via Particle Swarm Optimization (PSO). We show its application to a synthetic case in gravimetric inversion, performing at the same time the uncertainty analysis of the solution, which serves to improve the knowledge inferred about the geophysical anomalies. The application in real data to detect cavities has been also performed with excellent results

Anomaly shape inversion via model reduction and PSO

Most of the geophysical inverse problems in geophysical exploration consist in detecting, locating and outlining the shape of geophysical anomalous bodies imbedded into a quasi-homogeneous background by analyzing their effect in the geophysical signature. The inversion algorithm that is currently used creates a very fine mesh in the model space to approximate the shapes and the values of the anomalous bodies and the geophysical structure of the geological background. This approach results in discrete inverse problems with a huge uncertainty space, and the common way of stabilizing the inversion consists in introducing a reference model (through prior infor­ mation) to define the set of correctness of geophysical models. We present a different way of dealing with the high underdetermined character of this kind of problems, consisting in solving the inverse problem using a low dimensional parameterization that provides an approximate solution of the anomaly via Particle Swarm Opti­ mization (PSO). This methodology has been designed for anomaly detection in geological set-ups that correspond with this kind of problem. We show its application to synthetic and real cases in gravimetric inversion per­ forming at the same time uncertainty analysis of the solution. We have compared two different parameterizations for the geophysical anomalies (polygons and ellipses), showing that we have obtained similar results. This methodology outperforms the common least squares method with regularization