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Transportation infrastructure is of paramount importance for any country. The construction, management and maintenance of this infrastructure is a complex task that requires a significant amount of resources (e.g., human work equipment, materials, maintenance costs). To better support this task, in the last decades several Artificial Intelligence (AI) data analysis tools have been proposed. In this paper, we summarize recent predictive and prescriptive AI applications to the transportation infrastructure field, underlying their strategic impact. In particular, we discuss three case studies: the design of better earthwork projects; the prediction of jet grouting soilcrete mechanical and physical properties (uniaxial compressive strength, stiffness and column diameter); and prediction of the stability level of engineered slopes.
Joaquim Tinoco; Manuel Parente; António Gomes Correia; Paulo Cortez; David Toll. Predictive and prescriptive analytics in transportation geotechnics: Three case studies. Transportation Engineering 2021, 5, 100074 .
AMA StyleJoaquim Tinoco, Manuel Parente, António Gomes Correia, Paulo Cortez, David Toll. Predictive and prescriptive analytics in transportation geotechnics: Three case studies. Transportation Engineering. 2021; 5 ():100074.
Chicago/Turabian StyleJoaquim Tinoco; Manuel Parente; António Gomes Correia; Paulo Cortez; David Toll. 2021. "Predictive and prescriptive analytics in transportation geotechnics: Three case studies." Transportation Engineering 5, no. : 100074.
A key aspect that affect many deep underground mines over the world is the rockburst phenomenon, which can have a strong impact in terms of costs and lives. Accordingly, it is important their understanding in order to support decision makers when such events occur. One way to obtain a deeper and better understanding of the mechanisms of rockburst is through laboratory experiments. Hence, a database of rockburst laboratory tests was compiled, which was then used to develop predictive models for rockburst maximum stress and rockburst risk indexes through the application of soft computing techniques. The next step is to explore data gathered from in situ cases of rockburst. This study focusses on the analysis of such in situ information in order to build influence diagrams, enumerate the factors that interact in the occurrence of rockburst, and understand the relationships between them. In addition, the in situ rockburst data were also analyzed using different soft computing algorithms, namely artificial neural networks (ANNs). The aim was to predict the type of rockburst, that is, the rockburst level, based on geologic and construction characteristics of the mine or tunnel. One of the main observations taken from the study is that a considerable percentage of accidents occur as a result of excessive loads, generally at depths greater than 1000 m. In addition, it was also observed that soft computing algorithms can give an important contribution on determination of rockburst level, based on geologic and construction-related parameters.
Joaquim Tinoco; Luis Ribeiro e Sousa; Tiago Miranda; Rita Leal e Sousa. Rockburst Risk Assessment Based on Soft Computing Algorithms. Lecture Notes in Civil Engineering 2021, 703 -714.
AMA StyleJoaquim Tinoco, Luis Ribeiro e Sousa, Tiago Miranda, Rita Leal e Sousa. Rockburst Risk Assessment Based on Soft Computing Algorithms. Lecture Notes in Civil Engineering. 2021; ():703-714.
Chicago/Turabian StyleJoaquim Tinoco; Luis Ribeiro e Sousa; Tiago Miranda; Rita Leal e Sousa. 2021. "Rockburst Risk Assessment Based on Soft Computing Algorithms." Lecture Notes in Civil Engineering , no. : 703-714.
Keeping large-scale transportation infrastructure networks, such as railway networks, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance and network operation and the network dimension are two of the main factors that make the management of a transportation network such a challenging task. Hence, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition identification of rock cuttings slopes. It should be noted that one of the key points of the proposed system is to avoid data from complex monitoring equipment or laboratory expensive testes. Accordingly, only information taken from routine inspections (visual information) and complemented with geometric and geologic data will be used to feed the models. Therefore, in this work the flexible learning capabilities of Artificial Neural Networks (ANN) were used to fit a data-driven model for Earthwork Hazard Category (EHC) identification. Considering the high number of parameters involved in EHC identification, Genetic Algorithms (GA) were applied for input feature selection purposes. The proposed models were addressed following a nominal classification strategy. In addition, to overcome the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved modelling results are presented and discussed, detailing GA effectiveness and ANNs performance.
Joaquim Tinoco; António Gomes Correia; Paulo Cortez; David Toll. Combining Artificial Neural Networks and Genetic Algorithms for Rock Cuttings Slopes Stability Condition Identification. Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019 2019, 196 -209.
AMA StyleJoaquim Tinoco, António Gomes Correia, Paulo Cortez, David Toll. Combining Artificial Neural Networks and Genetic Algorithms for Rock Cuttings Slopes Stability Condition Identification. Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019. 2019; ():196-209.
Chicago/Turabian StyleJoaquim Tinoco; António Gomes Correia; Paulo Cortez; David Toll. 2019. "Combining Artificial Neural Networks and Genetic Algorithms for Rock Cuttings Slopes Stability Condition Identification." Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019 , no. : 196-209.
The prediction of the uniaxial compression strength (qu) of soil cement mixtures is of up most importance for design purposes. This is done traditionally by extensive laboratory tests which is time and resources consuming. In this paper, it is presented a new approach to assess qu over time based on the high learning capabilities of data mining techniques. A database of 444 records, encompassing cohesionless to cohesive and organic soils, different binder types, mixture conditions and curing time, were used to train three models based on support vector machines (SVMs), artificial neural networks (ANNs) and multiple regression. The results show a promising performance in qu prediction of laboratory soil cement mixtures, being the best results achieved with the SVM model (\(R^2 = 0.94\)) and with an average of SVM and ANN model (\(R^2 = 0.95\)), well reproducing the major effects of the input variables water/cement ratio, cement content, organic matter content and curing time, which are known as preponderant in soil cement mixtures behaviour.
Joaquim Tinoco; António Alberto; Paulo da Venda; António Gomes Correia; Luís Lemos. A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures. Neural Computing and Applications 2019, 32, 8985 -8991.
AMA StyleJoaquim Tinoco, António Alberto, Paulo da Venda, António Gomes Correia, Luís Lemos. A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures. Neural Computing and Applications. 2019; 32 (13):8985-8991.
Chicago/Turabian StyleJoaquim Tinoco; António Alberto; Paulo da Venda; António Gomes Correia; Luís Lemos. 2019. "A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures." Neural Computing and Applications 32, no. 13: 8985-8991.
The safety assessment of dams is a complex task that is made possible thanks to a constant monitoring of pertinent parameters. Once collected, the data are processed by statistical analysis models in order to describe the behaviour of the structure. The aim of those models is to detect early signs of abnormal behaviour so as to take corrective actions when required. Because of the uniqueness of each structure, the behavioural models need to adapt to each of these structures, and thus flexibility is required. Simultaneously, generalization capacities are sought, so a trade-off has to be found. This flexibility is even more important when the analysed phenomenon is characterized by nonlinear features. This is notably the case of the piezometric levels (PL) monitored at the rock–concrete interface of arch dams, when this interface opens. In that case, the linear models that are classically used by engineers show poor performances. Consequently, interest naturally grows for the advanced learning algorithms known as machine learning techniques. In this work, the aim was to compare the predictive performances and generalization capacities of six different data mining algorithms that are likely to be used for monitoring purposes in the particular case of the piezometry at the interface of arch dams: artificial neural networks (ANN), support vector machines (SVM), decision tree, k-nearest neighbour, random forest and multiple regression. All six are used to analyse the same time series. The interpretation of those PL permits to understand the phenomenon of the aperture of the interface, which is highly nonlinear, and of great concern in dam safety. The achieved results show that SVM and ANN stand out as the most efficient algorithms, when it comes to analysing nonlinear monitored phenomenon. Through a global sensitivity analysis, the influence of the models’ attributes is measured, showing a high impact of Z (relative trough) in PL prediction.
Joaquim Tinoco; Mathilde de Granrut; Daniel Dias; Tiago Miranda; Alexandre-Gilles Simon. Piezometric level prediction based on data mining techniques. Neural Computing and Applications 2019, 32, 4009 -4024.
AMA StyleJoaquim Tinoco, Mathilde de Granrut, Daniel Dias, Tiago Miranda, Alexandre-Gilles Simon. Piezometric level prediction based on data mining techniques. Neural Computing and Applications. 2019; 32 (8):4009-4024.
Chicago/Turabian StyleJoaquim Tinoco; Mathilde de Granrut; Daniel Dias; Tiago Miranda; Alexandre-Gilles Simon. 2019. "Piezometric level prediction based on data mining techniques." Neural Computing and Applications 32, no. 8: 4009-4024.
Joaquim Tinoco; António Alberto Correia; Paulo Venda Oliveira; António Gomes Correia; Luís Lemos. Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures. Geotecnia 2019, 145, 3 -16.
AMA StyleJoaquim Tinoco, António Alberto Correia, Paulo Venda Oliveira, António Gomes Correia, Luís Lemos. Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures. Geotecnia. 2019; 145 ():3-16.
Chicago/Turabian StyleJoaquim Tinoco; António Alberto Correia; Paulo Venda Oliveira; António Gomes Correia; Luís Lemos. 2019. "Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures." Geotecnia 145, no. : 3-16.
Joaquim Tinoco; António Alberto Correia; Paulo Venda Oliveira; Luís Lemos. Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures. Geotecnia 2019, 145, 3 -16.
AMA StyleJoaquim Tinoco, António Alberto Correia, Paulo Venda Oliveira, Luís Lemos. Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures. Geotecnia. 2019; 145 ():3-16.
Chicago/Turabian StyleJoaquim Tinoco; António Alberto Correia; Paulo Venda Oliveira; Luís Lemos. 2019. "Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures." Geotecnia 145, no. : 3-16.
This study aims to develop a tool able to help decision makers to find the best strategy for slopes management tasks. It is known that one of the main challenges nowadays for every developed or countries undergoing development is to keep operational under all conditions their transportations infrastructure. However, due to the network extension and increased budget constraints such challenge is even more difficult to accomplish. Keeping in mind the strong impact of a slope failure in the transportation infrastructure it is important to develop tools able to help minimizing this situation. Accordingly, and in order to achieve this goal, the high flexible learning capabilities of Artificial Neural Networks (ANNs) were applied in the development of a classification tool aiming to identify the stability condition of a rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspections activities (visual information) to feed them. For that, it was followed a nominal classification strategy and, in order to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE (Synthetic Minority Over-sampling Technique) and Oversampling. The achieved results are presented and discussed, comparing the achieved performance for both slope types (rock and soil cuttings) as well as the effect of the sampling approaches. An input-sensitivity analysis was applied, allowing to measure the relative influence of each model attribute.
Joaquim Tinoco; António Gomes Correia; Paulo Cortez; David Toll. Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction. Advancements in Geotechnical Engineering 2018, 105 -114.
AMA StyleJoaquim Tinoco, António Gomes Correia, Paulo Cortez, David Toll. Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction. Advancements in Geotechnical Engineering. 2018; ():105-114.
Chicago/Turabian StyleJoaquim Tinoco; António Gomes Correia; Paulo Cortez; David Toll. 2018. "Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction." Advancements in Geotechnical Engineering , no. : 105-114.
Joaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. Erratum for “Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing” by Joaquim Tinoco, A. Gomes Correia, Paulo Cortez, and David G. Toll. Journal of Computing in Civil Engineering 2018, 32, 08218001 .
AMA StyleJoaquim Tinoco, A. Gomes Correia, Paulo Cortez, David G. Toll. Erratum for “Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing” by Joaquim Tinoco, A. Gomes Correia, Paulo Cortez, and David G. Toll. Journal of Computing in Civil Engineering. 2018; 32 (5):08218001.
Chicago/Turabian StyleJoaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. 2018. "Erratum for “Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing” by Joaquim Tinoco, A. Gomes Correia, Paulo Cortez, and David G. Toll." Journal of Computing in Civil Engineering 32, no. 5: 08218001.
Keeping large-scale transportation infrastructure networks, such as railway networks, operational under all conditions is one of the major challenges today. The budgetary constraints for maintenance purposes and the network dimension are two of the main factors that make the management of a transportation network such a challenging task. Accordingly, aiming to assist the management of a transportation network, a data-driven model is proposed for stability condition prediction of embankment slopes. For such a purpose, the highly flexible learning capabilities of artificial neural networks (ANN) and support vector machines (SVM) were used to fit data-driven models for earthwork hazard category (EHC) prediction. Moreover, the data-driven models were created using visual information that is easy to collect during routine inspections. The proposed models were addressed following two different data modeling strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data (since typically good conditions are much common than bad ones), three training sampling approaches were explored: no resampling, synthetic minority oversampling technique (SMOTE), and oversampling. The achieved modeling results are presented and discussed, comparing the predictive performance of ANN and SVM algorithms, as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies was also carried out. Moreover, aiming at a better understanding of the proposed data-driven models, a detailed sensitivity analysis was applied, allowing the quantification of the relative importance of each model input, as well as measuring their global effect on the prediction of embankment stability conditions.
Joaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features. Journal of Computing in Civil Engineering 2018, 32, 04018027 .
AMA StyleJoaquim Tinoco, A. Gomes Correia, Paulo Cortez, David G. Toll. Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features. Journal of Computing in Civil Engineering. 2018; 32 (4):04018027.
Chicago/Turabian StyleJoaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. 2018. "Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features." Journal of Computing in Civil Engineering 32, no. 4: 04018027.
For transportation infrastructure, one of the greatest challenges today is to keep large-scale transportation networks, such as railway networks, operational under all conditions. This task becomes even more difficult to accomplish if one takes into account budget limitations for maintenance and repair works. This paper presents a tool aimed at helping in management tasks related to maintenance and repair work for a particular element of this infrastructure, the slopes. The highly flexible learning capabilities of artificial neural networks (ANNs) and support vector machines (SVMs) were applied in the development of a tool able to identify the stability condition of rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspection activities (visual information) to feed the models. This task was addressed following two different strategies: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, synthetic minority oversampling technique (SMOTE), and oversampling. The achieved results are presented and discussed, comparing the performance of ANN and SVM algorithms as well as the effect of the sampling approaches. A comparison between nominal classification and regression strategies for both rock and soil cutting slopes is also carried out, highlighting the different performance observed in the study of the two different types of slope.
Joaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing. Journal of Computing in Civil Engineering 2018, 32, 04017088 .
AMA StyleJoaquim Tinoco, A. Gomes Correia, Paulo Cortez, David G. Toll. Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing. Journal of Computing in Civil Engineering. 2018; 32 (2):04017088.
Chicago/Turabian StyleJoaquim Tinoco; A. Gomes Correia; Paulo Cortez; David G. Toll. 2018. "Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing." Journal of Computing in Civil Engineering 32, no. 2: 04017088.
This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system (VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating (RMR) system. Data mining (DM) techniques are applied to a database of volcanic rock geomechanical information from the islands. Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.
T. Miranda; L.R. Sousa; A.T. Gomes; Joaquim Tinoco; Cristiana Ferreira. Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques. Journal of Rock Mechanics and Geotechnical Engineering 2018, 10, 138 -150.
AMA StyleT. Miranda, L.R. Sousa, A.T. Gomes, Joaquim Tinoco, Cristiana Ferreira. Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques. Journal of Rock Mechanics and Geotechnical Engineering. 2018; 10 (1):138-150.
Chicago/Turabian StyleT. Miranda; L.R. Sousa; A.T. Gomes; Joaquim Tinoco; Cristiana Ferreira. 2018. "Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques." Journal of Rock Mechanics and Geotechnical Engineering 10, no. 1: 138-150.
Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining (DM) techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks (ANNs) to naive Bayesian classifiers. The aim was to predict the type of rockburst—that is, the rockburst level—based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.info:eu-repo/semantics/publishedVersio
Luis Ribeiro e Sousa; Tiago Miranda; Rita Leal e Sousa; Joaquim Tinoco. The Use of Data Mining Techniques in Rockburst Risk Assessment. Engineering 2017, 3, 552 -558.
AMA StyleLuis Ribeiro e Sousa, Tiago Miranda, Rita Leal e Sousa, Joaquim Tinoco. The Use of Data Mining Techniques in Rockburst Risk Assessment. Engineering. 2017; 3 (4):552-558.
Chicago/Turabian StyleLuis Ribeiro e Sousa; Tiago Miranda; Rita Leal e Sousa; Joaquim Tinoco. 2017. "The Use of Data Mining Techniques in Rockburst Risk Assessment." Engineering 3, no. 4: 552-558.
The aim of this paper is to demonstrate the advanced tools and techniques used for adding value to the soil stabilization practice. The tools presented involve advanced laboratory tests and modeling using codes and soft computing to evaluate the mechanical behavior of stabilized soils with cement, ranging from short-term to long-term behavior. More precisely, these tools are able to: 1. Predict the mechanical behavior of the stabilized soils over time from data obtained in the early ages saving time in laboratory tests; 2. Predict the mechanical behavior of the stabilized soils over time based on basic parameters of soil type and binder using historical accurate data, avoiding mechanical laboratory tests. 3. Incorporate the serviceability limit state concept in a novel proposal to estimate the design modulus in function of the uniaxial compressive strength and the strain level, making more economic and sustainable geotechnical solutions.
António Gomes Correia; Joaquim Tinoco. Advanced tools and techniques to add value to soil stabilization practice. Innovative Infrastructure Solutions 2017, 2, 1 .
AMA StyleAntónio Gomes Correia, Joaquim Tinoco. Advanced tools and techniques to add value to soil stabilization practice. Innovative Infrastructure Solutions. 2017; 2 (1):1.
Chicago/Turabian StyleAntónio Gomes Correia; Joaquim Tinoco. 2017. "Advanced tools and techniques to add value to soil stabilization practice." Innovative Infrastructure Solutions 2, no. 1: 1.
One of the main awareness for a road infrastructures manager is to increase its efficiency under limited resources. Pavement Management Systems aim, at last, to support road administrations in the decision-making process regarding its management policy and long-term strategies for maintenance and rehabilitation activities. While several road administrations are putting efforts in developing optimisation methodologies to enhance their decision making process, many still lack of data that allows the development of reliable prediction models for pavement performance. This is a key aspect to develop and test decision-making methodologies. Although there are several prediction models available in the literature, their practical applications are often limited to the very specific network from which data were retrieved at first and to a specific performance indicator (PI). This paper presents a practical application of a Markov model to predict the evolution of five PIs – cracking, skid resistance, bearing capacity, longitudinal evenness and transverse evenness – and consequent combined PIs, using historical data from an extensive pavement database. The conversion for PIs is made through a standardisation procedure proposed by an European COST Action, which may be considered a reference classification system for road administrations. The presented model is intended to be an useful input for researchers and administrations willing to develop and test different optimisation approaches.
André Moreira; Joaquim Tinoco; Joel R. M. Oliveira; Adriana Santos. An application of Markov chains to predict the evolution of performance indicators based on pavement historical data. International Journal of Pavement Engineering 2016, 19, 937 -948.
AMA StyleAndré Moreira, Joaquim Tinoco, Joel R. M. Oliveira, Adriana Santos. An application of Markov chains to predict the evolution of performance indicators based on pavement historical data. International Journal of Pavement Engineering. 2016; 19 (10):937-948.
Chicago/Turabian StyleAndré Moreira; Joaquim Tinoco; Joel R. M. Oliveira; Adriana Santos. 2016. "An application of Markov chains to predict the evolution of performance indicators based on pavement historical data." International Journal of Pavement Engineering 19, no. 10: 937-948.
This study takes advantage of the high learning capabilities of data mining (DM) techniques towards to the development of a novel approach for jet grouting (JG) column diameter prediction. The high number of variables involved in JG technology as well as the complex phenomena related with the injection process make JG column diameter (D) prediction a difficult task. Therefore, in order to overcome it, the flexible learning capabilities of DM techniques were applied as an alternative approach of the traditional tools. The achieved results show that both artificial neural network and support vector machine algorithms can be trained to accurately predict D built in different soil types of clayey nature and using different JG systems. In both cases a coefficient of correlation () very close to the unity was achieved. For models training, a set of eight input variables were considered. Among them, the rod withdrawal speed, flow rate of the grout slurry and the JG system were identified as the most relevant ones, although the grout pressure and the dynamic impact of the grout also revealed an important influence on D prediction. Moreover, additionally to the identification of the key model variables, it was also measured their effects on D prediction based on a data-based sensitivity analysis. These achievements represent a novel contribution for JG technology, mainly at the design level. Furthermore, the obtained results also underline the potential and contribution of DM to solving complex problem in geotechnical engineering.
Joaquim Tinoco; A. Gomes Correia; Paulo Cortez. Jet grouting column diameter prediction based on a data-driven approach. European Journal of Environmental and Civil Engineering 2016, 22, 338 -358.
AMA StyleJoaquim Tinoco, A. Gomes Correia, Paulo Cortez. Jet grouting column diameter prediction based on a data-driven approach. European Journal of Environmental and Civil Engineering. 2016; 22 (3):338-358.
Chicago/Turabian StyleJoaquim Tinoco; A. Gomes Correia; Paulo Cortez. 2016. "Jet grouting column diameter prediction based on a data-driven approach." European Journal of Environmental and Civil Engineering 22, no. 3: 338-358.
Roman Denysiuk; José C. Matos; Joaquim Tinoco; Tiago Miranda; António Gomes Correia. Multiobjective Optimization of Maintenance Scheduling: Application to Slopes and Retaining Walls. Procedia Engineering 2016, 143, 666 -673.
AMA StyleRoman Denysiuk, José C. Matos, Joaquim Tinoco, Tiago Miranda, António Gomes Correia. Multiobjective Optimization of Maintenance Scheduling: Application to Slopes and Retaining Walls. Procedia Engineering. 2016; 143 ():666-673.
Chicago/Turabian StyleRoman Denysiuk; José C. Matos; Joaquim Tinoco; Tiago Miranda; António Gomes Correia. 2016. "Multiobjective Optimization of Maintenance Scheduling: Application to Slopes and Retaining Walls." Procedia Engineering 143, no. : 666-673.
In this paper a new data-driven approach is proposed for uniaxial compressive strength (qu) prediction of laboratory soil-cement mixtures. The proposed model is able to predict qu over time under different conditions, e.g. different cement contents or soil types, and can be applied at the pre-design stage. This means that the model can be applied previously to the preparation of any laboratory formulation. The designer only needs to collect information about the main geotechnical soil properties (grain size, organic matter content, among other) and select the binder composition to prepare the mixture.Based on a sensitivity analysis, the key model variables were identified and its effect quantified. Thus, it was caught by the model the most relevant variables in qu prediction over time and very high prediction capacity with an overall regression coefficient higher than 0.95
Joaquim Tinoco; António Alberto; Paulo da Venda; Antonio Correia; Luís Lemos. A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures. Procedia Engineering 2016, 143, 566 -573.
AMA StyleJoaquim Tinoco, António Alberto, Paulo da Venda, Antonio Correia, Luís Lemos. A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures. Procedia Engineering. 2016; 143 ():566-573.
Chicago/Turabian StyleJoaquim Tinoco; António Alberto; Paulo da Venda; Antonio Correia; Luís Lemos. 2016. "A Data-driven Approach for qu Prediction of Laboratory Soil-cement Mixtures." Procedia Engineering 143, no. : 566-573.
The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.
Elisabete Freitas; Joaquim Tinoco; Francisco Soares; Jocilene Costa; Paulo Cortez; Paulo Pereira. Modelling Tyre-Road Noise with Data Mining Techniques. Archives of Acoustics 2015, 40, 547 -560.
AMA StyleElisabete Freitas, Joaquim Tinoco, Francisco Soares, Jocilene Costa, Paulo Cortez, Paulo Pereira. Modelling Tyre-Road Noise with Data Mining Techniques. Archives of Acoustics. 2015; 40 (4):547-560.
Chicago/Turabian StyleElisabete Freitas; Joaquim Tinoco; Francisco Soares; Jocilene Costa; Paulo Cortez; Paulo Pereira. 2015. "Modelling Tyre-Road Noise with Data Mining Techniques." Archives of Acoustics 40, no. 4: 547-560.
An empirical system was developed to obtain a quality index for rock slopes in road infrastructures, named Slope Quality Index (SQI), and it was applied to a set of real slopes.The SQI is supported in nine factors affecting slope stability that contemplate the evaluation of different parameters. Consequently, each factor is classified by the degree of importance and influence by assigned weights. These weights were established through a statistical analysis of replies to a survey that was distributed to several experienced professionals in the field. The proposed SQI varies between1 and 5, corresponding to slopes in very good and very bad condition state, respectively. Besides the advantage linked to a quantitative and qualitative evaluation of slopes, theSQI also allows identifying the most critical factors on the slope stability, which is a fundamental issue for an efficient management of the slope network in the road infrastructure, namely in the planning of conservation and maintenance operations.FEDERThe authors would like to thank AdI — Innovation Agency, for the financial support awarded through POFC program, for the R&D project SustIMS — Sustainable Infrastructure Management Systems (FCOMP01-0202-FEDER-023113)
Marisa Pinheiro; Sara Sanches; Tiago Miranda; Adriana Neves; Joaquim Tinoco; Alexandra Ferreira; António Gomes Correia. A new empirical system for rock slope stability analysis in exploitation stage. International Journal of Rock Mechanics and Mining Sciences 2015, 76, 182 -191.
AMA StyleMarisa Pinheiro, Sara Sanches, Tiago Miranda, Adriana Neves, Joaquim Tinoco, Alexandra Ferreira, António Gomes Correia. A new empirical system for rock slope stability analysis in exploitation stage. International Journal of Rock Mechanics and Mining Sciences. 2015; 76 ():182-191.
Chicago/Turabian StyleMarisa Pinheiro; Sara Sanches; Tiago Miranda; Adriana Neves; Joaquim Tinoco; Alexandra Ferreira; António Gomes Correia. 2015. "A new empirical system for rock slope stability analysis in exploitation stage." International Journal of Rock Mechanics and Mining Sciences 76, no. : 182-191.