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Francisco Ortega-Fernández
Project Engineering Department, University of Oviedo, 33004 Oviedo, Spain

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Journal article
Published: 13 July 2021 in Materials
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Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or corrosion loss prediction result. The aim of this research is to develop predictive models to estimate corrosion loss based on easily worldwide available parameters. Experimental data from internationally validated studies were used for the data mining process, basing their characterisation on seven globally accessible qualitative and quantitative variables. Self-Organising Maps including both supervised and unsupervised layers were used to predict first-year corrosion loss, its corrosivity categories, and an uncertainty range. Additionally, a formula optimised with Newton’s method has been proposed for extrapolating these results to long-term results. The predictions obtained were compared with real values using Euclidean distances to know its similarity degree, offering high prediction performance. Specifically, evaluation results showed an average saving of up to 16% in coatings using these predictions. Therefore, using the proposed models reduces the uncertainty of the final structures state by predicting their material loss, avoiding initial over-dimensioning of structures, and meeting the principles of efficiency and sustainability, thus reducing costs.

ACS Style

Marta Terrados-Cristos; Francisco Ortega-Fernández; Guillermo Alonso-Iglesias; Marina Díaz-Piloneta; Ana Fernández-Iglesias. Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques. Materials 2021, 14, 3906 .

AMA Style

Marta Terrados-Cristos, Francisco Ortega-Fernández, Guillermo Alonso-Iglesias, Marina Díaz-Piloneta, Ana Fernández-Iglesias. Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques. Materials. 2021; 14 (14):3906.

Chicago/Turabian Style

Marta Terrados-Cristos; Francisco Ortega-Fernández; Guillermo Alonso-Iglesias; Marina Díaz-Piloneta; Ana Fernández-Iglesias. 2021. "Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques." Materials 14, no. 14: 3906.

Journal article
Published: 27 April 2021 in Energies
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One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting.

ACS Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies 2021, 14, 2486 .

AMA Style

Vanesa Mateo-Pérez, Marina Corral-Bobadilla, Francisco Ortega-Fernández, Vicente Rodríguez-Montequín. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies. 2021; 14 (9):2486.

Chicago/Turabian Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. 2021. "Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms." Energies 14, no. 9: 2486.

Journal article
Published: 02 March 2021 in Journal of Marine Science and Engineering
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One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.

ACS Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain). Journal of Marine Science and Engineering 2021, 9, 267 .

AMA Style

Vanesa Mateo-Pérez, Marina Corral-Bobadilla, Francisco Ortega-Fernández, Vicente Rodríguez-Montequín. Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain). Journal of Marine Science and Engineering. 2021; 9 (3):267.

Chicago/Turabian Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Vicente Rodríguez-Montequín. 2021. "Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)." Journal of Marine Science and Engineering 9, no. 3: 267.

Journal article
Published: 08 February 2021 in Water
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The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision ( = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers.

ACS Style

Vanesa Mateo Pérez; José Mesa Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Gross Solids Content Prediction in Urban WWTPs Using SVM. Water 2021, 13, 442 .

AMA Style

Vanesa Mateo Pérez, José Mesa Fernández, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Gross Solids Content Prediction in Urban WWTPs Using SVM. Water. 2021; 13 (4):442.

Chicago/Turabian Style

Vanesa Mateo Pérez; José Mesa Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2021. "Gross Solids Content Prediction in Urban WWTPs Using SVM." Water 13, no. 4: 442.

Journal article
Published: 06 January 2021 in Sustainability
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Many organizations are currently face significant challenges in terms of sustainability and technological development. Achieving sustainability in business activities, interweaving social, economic, and environmental perspectives, is one of the most challenging goals for companies. On the other hand, as technology advances exponentially, organizations grow in a linear way. This fact causes a gap which increases over the time. Models and tools have been developed to try to solve both problems separately; on one side to make the organization grow exponentially, and on the other side to incorporate sustainability into the business model. However, they do not allow enough time to know if the actions carried out really achieve their aim. The model presented provides a solution to both problems by monitoring the evolution of organizations towards an exponential structure through the analysis of the project portfolio. The main objective is to know how the orientation of ongoing projects has changed during the last period, in order to position them in terms of achieving the desired sustainability-oriented transformation. With the model designed, it is possible to know if the actions developed by the company are really heading towards a sustainable model and exponential growth. With the aim of validating the model, it has been applied in an innovation organization. With this model, the level of exponential progress of the organization was determined, as well as the goals that have been attained best and worst so far.

ACS Style

Marina Díaz-Piloneta; Francisco Ortega-Fernández; Henar Morán-Palacios; Vicente Rodríguez-Montequín. Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study. Sustainability 2021, 13, 464 .

AMA Style

Marina Díaz-Piloneta, Francisco Ortega-Fernández, Henar Morán-Palacios, Vicente Rodríguez-Montequín. Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study. Sustainability. 2021; 13 (2):464.

Chicago/Turabian Style

Marina Díaz-Piloneta; Francisco Ortega-Fernández; Henar Morán-Palacios; Vicente Rodríguez-Montequín. 2021. "Monitoring the Implementation of Exponential Organizations through the Assessment of Their Project Portfolio: Case Study." Sustainability 13, no. 2: 464.

Journal article
Published: 26 November 2020 in Sustainability
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Corrosion is the main mechanism of the degradation of steel structures buried in the soil. Due to its aggressiveness, the material gradually loses thickness until the structure fails, which may cause serious environmental problems. The lack of a clearly established method in the design leads to the need for conservative excess thicknesses to ensure their useful life. This implies inefficient use of steel and an increase in the cost of the structure. In this paper, four quantitative and multivariate models were created to predict the loss of buried steel as a function of time. We developed a basic model, as well as a physical and an electrochemical one, based on multivariate adaptive regression spline (MARS), and a simpler model for comparative purposes based on clusters with Euclidean distance. The modeling was synthesized in a computer tool where the inputs were the characteristics of the soil and the time and the outputs were the loss of thickness of each predictive model and the description of the most similar real tests. The results showed that in all models, for relative errors of 10%, over 90% of predictions were correct. In addition, a real example of the operation of the tool was defined, where it was found that the estimates of the models allow the necessary optimization of steel to fulfill its useful life.

ACS Style

Lorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability 2020, 12, 9879 .

AMA Style

Lorena-De Arriba-Rodríguez, Vicente Rodríguez-Montequín, Joaquín Villanueva-Balsera, Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability. 2020; 12 (23):9879.

Chicago/Turabian Style

Lorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. 2020. "Design of Predictive Models to Estimate Corrosion in Buried Steel Structures." Sustainability 12, no. 23: 9879.

Journal article
Published: 03 September 2020 in Sustainability
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In order to enlarge the shelf life and avoid the waste of fresh-cut (FC) products, novel packaging techniques with antimicrobial properties have been proposed. In this work, we analyzed the potential environmental benefits of using films reinforced with bactericidal ZnO nanoparticles (NP) for FC produce packaging, when compared to the traditional polypropylene (PP) films. A biodegradable, polylactic acid (PLA) package and a non-biodegradable, polypropylene package, both coated with ZnO NP, were considered as novel technologies. The eco-profile of the considered alternatives was assessed via two life cycle assessments (LCAs). Firstly, an attributional LCA was performed in order to compare the materials in terms of their production and end of life (EOL) processes, allowing us to extend the conclusions to different food products. Secondly, a consequential LCA was performed taking into account the whole life cycle of the fresh vegetable, with special attention to the environmental implications of the produce losses among the chain. The uncertainties of the models were assessed via Monte Carlo approach. In both cases, the scenarios concerning the PLA and PP active packages with ZnO NP showed a better profile than the traditional techniques, specifically when considering the full supply chain of the FC vegetables in the consequential LCA. As agricultural production is the main contributor to the environmental impact of the cycle, the avoidance of wastes by extending the shelf life through the novel packages leads to the impact reduction of FC products.

ACS Style

Miguel Vigil; Maria Pedrosa-Laza; Jv Alvarez Cabal; Francisco Ortega-Fernández. Sustainability Analysis of Active Packaging for the Fresh Cut Vegetable Industry by Means of Attributional & Consequential Life Cycle Assessment. Sustainability 2020, 12, 7207 .

AMA Style

Miguel Vigil, Maria Pedrosa-Laza, Jv Alvarez Cabal, Francisco Ortega-Fernández. Sustainability Analysis of Active Packaging for the Fresh Cut Vegetable Industry by Means of Attributional & Consequential Life Cycle Assessment. Sustainability. 2020; 12 (17):7207.

Chicago/Turabian Style

Miguel Vigil; Maria Pedrosa-Laza; Jv Alvarez Cabal; Francisco Ortega-Fernández. 2020. "Sustainability Analysis of Active Packaging for the Fresh Cut Vegetable Industry by Means of Attributional & Consequential Life Cycle Assessment." Sustainability 12, no. 17: 7207.

Journal article
Published: 27 June 2020 in Remote Sensing
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Knowledge of the free draft of ports is essential for the adequate management of ports. To maintain these drafts, it is necessary to carry out dredging periodically, and to conduct bathymetries using traditional techniques, such as echo sounding. However, an echo sounder is very expensive and its accuracy is subject to weather conditions. Thus, the use of recent advancements in remote sensing techniques provide a better solution for mapping and estimating the evolution of the seabed in these areas. This paper presents a cost-effective and practical method for estimating satellite-derived bathymetry for highly polluted and turbid waters at two different ports in the cities of Luarca and Candás in the Principality of Asturias (Spain). The method involves the use of the support vector machine (SVM) technique and open Sentinel-2 satellite imagery, which the European Space Agency has supplied. Models were compared to the bathymetries that were obtained from the in situ data collected by a single beam echo sounder that the Port Service of the Principality of Asturias provided. The most accurate values of the training and testing dataset in Candás, were R2 = 0.911 and RMSE = 0.3694 m, and R2 = 0.8553 and RMSE = 0.4370 m, respectively. The accuracies of the training and testing dataset values in Luarca were R2 = 0.976 and RMSE = 0.4409 m, and R2 = 0.9731 and RMSE = 0.4640 m, respectively. The regression analysis results of the training and testing dataset were consistent. The approaches that have been developed in this work may be included in the monitoring of future dredging activities in ports, especially where the water is polluted, muddy and highly turbid.

ACS Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Eliseo Vergara-González. Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery. Remote Sensing 2020, 12, 2069 .

AMA Style

Vanesa Mateo-Pérez, Marina Corral-Bobadilla, Francisco Ortega-Fernández, Eliseo Vergara-González. Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery. Remote Sensing. 2020; 12 (13):2069.

Chicago/Turabian Style

Vanesa Mateo-Pérez; Marina Corral-Bobadilla; Francisco Ortega-Fernández; Eliseo Vergara-González. 2020. "Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery." Remote Sensing 12, no. 13: 2069.

Journal article
Published: 11 May 2020 in Water
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The pre-treatment stage of wastewater treatment plants (WWTP), where most of the larger waste, including sand and fat, is removed, is of great importance for the performance and durability of these plants. This work develops a model that predicts the sand content that reaches the plant. For this purpose, data were collected from one operation year of the Villapérez Wastewater Treatment Plant located in the northeast of the city of Oviedo (Asturias, Spain) and the MARS (Multivariate Adaptive Regression Splines) method was used for modelling. The accuracy of the MARS model developed using the determination coefficient is R2 = 0.74 for training data and R2 = 0.70 in validation data. These results indicate that it is possible to predict trend changes in sand production as a function of input variables changes such as flow rate, pH, ammonia, etc. This will prevent the plant from possible operational problems, as actions could be taken, such as starting up more pre-treatment lines or emptying the containers, so that the arrival of the sand can be assumed without any problem. In this way, the possibility of letting sand contents over the established limits pass that could affect the following processes of the treatment plant is avoided.

ACS Style

Vanesa Mateo Pérez; José Manuel Mesa Fernández; Francisco Ortega Fernández; Henar Morán Palacios. Sand Content Prediction in Urban WWTPs Using MARS. Water 2020, 12, 1357 .

AMA Style

Vanesa Mateo Pérez, José Manuel Mesa Fernández, Francisco Ortega Fernández, Henar Morán Palacios. Sand Content Prediction in Urban WWTPs Using MARS. Water. 2020; 12 (5):1357.

Chicago/Turabian Style

Vanesa Mateo Pérez; José Manuel Mesa Fernández; Francisco Ortega Fernández; Henar Morán Palacios. 2020. "Sand Content Prediction in Urban WWTPs Using MARS." Water 12, no. 5: 1357.

Journal article
Published: 21 July 2019 in Applied Sciences
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Rare earths have appeared in the market with new energy and Information Technology and Communications (ITC) applications. While their demand grows exponentially, their production is experiencing a bottleneck given that their deposits are concentrated in very few locations, mainly in China. This scarcity and dependence have turned them into strategic minerals, and the location of new sources has become vital. On the other hand, the inevitable trend towards sustainability favors the reuse of waste to avoid the degradation of new areas and the need for waste storage. One of the biggest generators of waste is iron mining. The tailings are stored in huge ponds with consequent environmental problems and risks. As tailings come from a concentration process, they incorporate different amounts of rare earths depending on their separation behavior. To evaluate the viability of these resources as potential repositories of rare earths, samples of different types of deposits and treatments were selected. The presence of different rare earths in them was determined through spectroscopy techniques to evaluate their use as a deposit. The results show an increase in the concentration of rare earths, especially high-density ones, which, although currently not economically feasible given the very wide geographical distribution of iron mining, represent a fundamental strategic reserve.

ACS Style

Henar Morán Palacios; Francisco Ortega-Fernandez; Raquel Lopez-Castaño; Jose V. Alvarez-Cabal. The Potential of Iron Ore Tailings as Secondary Deposits of Rare Earths. Applied Sciences 2019, 9, 2913 .

AMA Style

Henar Morán Palacios, Francisco Ortega-Fernandez, Raquel Lopez-Castaño, Jose V. Alvarez-Cabal. The Potential of Iron Ore Tailings as Secondary Deposits of Rare Earths. Applied Sciences. 2019; 9 (14):2913.

Chicago/Turabian Style

Henar Morán Palacios; Francisco Ortega-Fernandez; Raquel Lopez-Castaño; Jose V. Alvarez-Cabal. 2019. "The Potential of Iron Ore Tailings as Secondary Deposits of Rare Earths." Applied Sciences 9, no. 14: 2913.

Journal article
Published: 24 January 2019 in Journal of Cleaner Production
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Steel making processes dispose large volumes of waste gases whose potential energy can be transformed into heat and electricity by means of cogeneration. A case study using coke oven and Linz-Donawitz converter gas is presented here. The data is obtained from an existing plant located in Northern Spain. The engines are adapted for its operation with converter gas, and steam is generated in boilers that consume coke gas, converter gas and natural gas in the absence of waste gases. The actual influence on the environmental behaviour of the process is analysed considering the benefits but also the drawbacks derived from the gases low calorific value, toxicity and polluting. The work constitutes the first study in an installation with these characteristics and burning this combination of gases. The functional unit is represented by 1 MWh of thermal energy. The analysis has been developed mainly on the bases of the following sources: site-specific measured or calculated data directly from the process of cogeneration, life-cycle inventory databases and bibliographical information. Operating parameters, as well as production data (thermal and electric energy), emissions of NOX, SO2 and CO2, discharges and wastes associated with this process are exposed. The system boundaries were considered gate to gate, so the results are useful for the integration with other global scenarios. Midpoint and endpoint characterisation factors for humans, ecosystems and resources are given. The main effects are related to Climate change, Ionising radiation, Human toxicity and Fossil and Ozone depletion. The results indicate that the usage of these gases implies an environmental benefit. The operational data belonging to 2014 shows a reduction of more than 100 points in the global impact of the functional unit. In the best scenario, 169.42 Nm3/MWh of natural gas may be saved with the consequent reduction in natural resources and ozone depletion.

ACS Style

Sergio García García; Vicente Rodríguez Montequín; Rocío Luiña Fernández; Francisco Ortega Fernández. Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study. Journal of Cleaner Production 2019, 217, 576 -583.

AMA Style

Sergio García García, Vicente Rodríguez Montequín, Rocío Luiña Fernández, Francisco Ortega Fernández. Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study. Journal of Cleaner Production. 2019; 217 ():576-583.

Chicago/Turabian Style

Sergio García García; Vicente Rodríguez Montequín; Rocío Luiña Fernández; Francisco Ortega Fernández. 2019. "Evaluation of the synergies in cogeneration with steel waste gases based on Life Cycle Assessment: A combined coke oven and steelmaking gas case study." Journal of Cleaner Production 217, no. : 576-583.

Review
Published: 09 May 2018 in Metals
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Around the world, there are thousands of metal structures completely or partially buried in the soil. The main concern in their design is corrosion. Corrosion is a mechanism that degrades materials and causes structural failures in infrastructures, which can lead to severe effects on the environment and have direct impact on the population health. In addition, corrosion is extremely complex in the underground environment due to the variability of the local conditions. The problem is that there are many methods to its evaluation but none have been clearly established. In order to ensure the useful life of such structures, engineers usually consider an excess thickness that increases the economic cost of manufacturing and does not satisfy the principles of efficiency in the use of resources. In this paper, an extended revision of the existing methods to evaluate corrosion is carried out to optimize the design of buried steel structures according to their service life. Thus, they are classified into two categories depending on the information they provide: qualitative and quantitative methods. As a result, it is concluded that the most exhaustive methodologies for estimating soil corrosion are quantitative methods fed by non-electrochemical data based on experimental studies that measure the mass loss of structures.

ACS Style

Lorena-De Arriba-Rodriguez; Joaquin Villanueva-Balsera; Francisco Ortega-Fernandez; Fernando Rodriguez-Perez. Methods to Evaluate Corrosion in Buried Steel Structures: A Review. Metals 2018, 8, 334 .

AMA Style

Lorena-De Arriba-Rodriguez, Joaquin Villanueva-Balsera, Francisco Ortega-Fernandez, Fernando Rodriguez-Perez. Methods to Evaluate Corrosion in Buried Steel Structures: A Review. Metals. 2018; 8 (5):334.

Chicago/Turabian Style

Lorena-De Arriba-Rodriguez; Joaquin Villanueva-Balsera; Francisco Ortega-Fernandez; Fernando Rodriguez-Perez. 2018. "Methods to Evaluate Corrosion in Buried Steel Structures: A Review." Metals 8, no. 5: 334.

Journal article
Published: 30 March 2017 in Sustainability
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This paper presents a case study of how a Spanish financial software factory (FSF) has determined the weights of the indicators and objectives included in their strategy map with the aim of ensuring its business sustainability. A strategy map is a graphical representation of the cause-effect relationships between strategic objectives and indicators of a balanced scorecard (BSC). The detailed description of the strategy map development and deployment is not part of the aim of this work as it was described in a former paper. In this study, FAHP, a multicriteria decision-making (MCDM) method using the concepts of fuzzy set theory and hierarchical structure analysis, was used to calculate the weights. The analysis was carried out considering the points of view of different groups of stakeholders (shareholders, top management, middle managers, other employees, customers and some experts in the field of software factories) and the results are presented grouped by role to get a better understanding of the preferences of each kind of stakeholder. The conclusions of this study give a better insight of the corporative sustainability strategies of this kind of firms as well as the different vision of each stakeholder, what could be very valuable to the software factory managers for the decision-making and the strategic management of their organizations.

ACS Style

César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector. Sustainability 2017, 9, 527 .

AMA Style

César Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector. Sustainability. 2017; 9 (4):527.

Chicago/Turabian Style

César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector." Sustainability 9, no. 4: 527.

Journal article
Published: 24 March 2017 in Sustainability
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A balanced scorecard (BSC) framework for a factory that develops software for banking was proposed by us at the end of 2015 to ensure its sustainability, and was focused on improving its productivity and cost. Based on this framework, the aim of this study is to construct an approach using the analytic hierarchy process (AHP) and BSC for evaluating a factory’s performance in order for it to become a sustainable business. In this study, AHP is proposed to prioritise and determine weights for the perspectives and indicators included in the BSC for a financial software factory (FSF). The combination of these weights with different indicator measures produces a model that provides an effective assessment tool for FSF managers. The results of the study, which are shown both globally and disaggregated according to the different roles of FSF stakeholders, show that user satisfaction is the main pillar for making decisions. In addition, the result considering roles shows differences according to the relationship of each stakeholder with the software factory. The current study has been validated in a Spanish factory that develops software for several financial entities.

ACS Style

César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability 2017, 9, 486 .

AMA Style

César Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability. 2017; 9 (4):486.

Chicago/Turabian Style

César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector." Sustainability 9, no. 4: 486.

Journal article
Published: 02 December 2015 in Sustainability
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Financial institutions and especially banks have always been at the forefront of innovation in management policies in order to improve their performance, and banking is probably one of the sectors that more effectively measures productivity and efficiency in virtually all aspects of its business. However, there is one area that still fails: the productivity of its software development projects. For years banking institutions have chosen to outsource their software projects using software firms created by them for this purpose, but up until a few years ago, the deadline for the delivery of the projects was more important than the efficiency with which they were developed. The last economic crisis has forced financial institutions to review and improve the software development efficiency related to their software factories to achieve a sustainable and feasible model. The sustainability of these software factories can be achieved by improving their strategic management, and the Balanced Scorecard (BSC) framework can be very useful in order to obtain this. Based on the concepts and practices of the BSC, this paper proposes a specific model to establish this kind of software factory as a way of improving their sustainability and applies it to a large Spanish firm specializing in financial sector software. We have included a preliminary validation plan as well as the first monitoring results. The adoption is still very recent and more data are needed to measure all the perspectives so no definitive conclusions can be drawn.

ACS Style

César Álvarez; Vicente Rodríguez; Francisco Ortega; Joaquín Villanueva. A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector. Sustainability 2015, 7, 15999 -16021.

AMA Style

César Álvarez, Vicente Rodríguez, Francisco Ortega, Joaquín Villanueva. A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector. Sustainability. 2015; 7 (12):15999-16021.

Chicago/Turabian Style

César Álvarez; Vicente Rodríguez; Francisco Ortega; Joaquín Villanueva. 2015. "A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector." Sustainability 7, no. 12: 15999-16021.