Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

Tinoco, Joaquim Agostinho Barbosa; Correia, A. Gomes; Cortez, Paulo; Toll, D. G.

doi:10.1061/(asce)cp.1943-5487.0000739

Cited by 16 publications

(16 citation statements)

References 38 publications

(37 reference statements)

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“…Thus, by assessing features' relevance, both scholars and practitioners can discern what influences guest satisfaction, which can leverage decision making. This study proposes using the data-based sensitivity analysis (DSA), which was first introduced by Cortez and Embrechts (2013) and has since been applied to wide number of cases, including tourism (e.g., Moro et al, 2017), civil engineering (Tinoco et al, 2017), and marketing (Romão et al, 2019). The DSA takes a randomly selected sample of data from the training dataset, and then uses it by simultaneously varying the input features through their range of possible values to assess output sensitivity to such input changes (Cortez and Embrechts, 2013).…”

Section: Resultsmentioning

confidence: 99%

Evaluating a guest satisfaction model through data mining

Moro

Esmerado

Ramos

et al. 2019

IJCHM

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Purpose This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

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Section: Resultsmentioning

confidence: 99%

Evaluating a guest satisfaction model through data mining

Moro

Esmerado

Ramos

et al. 2019

IJCHM

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“…In addition, determining whether a slope will fail or not is frequently a difficult task involving many variables and high dimensionality. In order to work around these kinds of restrictions and aid in the decision-making process for large-scale network management, a first endeavor was recently made [30,31] by comparing two popular types of data mining (DM) algorithms: artificial neural betworks (ANNs) [32,33] and support vector machines (SVMs) [34]. When compared with other simpler learning models (e.g., multiple regression or logistic regression models), the ANN and SVM models are more flexible learners, being capable of learning complex input-to-output mappings.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…When compared with other simpler learning models (e.g., multiple regression or logistic regression models), the ANN and SVM models are more flexible learners, being capable of learning complex input-to-output mappings. We particularly note that the two initial slope stability prediction studies [30,31] assumed a fixed set of selected input variables that could easily be collected during routine inspection activities. The main goal was to compare two learning models (ANN and SVM) for both regression and classification tasks.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

An Evolutionary Neural Network Approach for Slopes Stability Assessment

Tinoco,

Gomes Correia,

Cortez

et al. 2023

Applied Sciences

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A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a first attempt at the development of a classification system for rock and soil cuttings, as well as embankments based on visual features, was made in this paper using soft computing algorithms. The achieved results, although interesting, nevertheless have some important limitations to their successful use as auxiliary tools for transportation network management tasks. Accordingly, we carried out new experiments through the combination of modern optimization and soft computing algorithms. Thus, one of the main challenges to overcome is related to the selection of the best set of input features for a feedforward neural network for earthwork hazard category (EHC) identification. We applied a genetic algorithm (GA) for this purpose. Another challenging task is related to the asymmetric distribution of the data (since typically good conditions are much more common than bad ones). To address this question, three training sampling approaches were explored: no resampling, the synthetic minority oversampling technique (SMOTE), and oversampling. Some relevant observations were taken from the optimization process, namely, the identification of which variables are more frequently selected for EHC identification. After finding the most efficient models, a detailed sensitivity analysis was applied over the selected models, allowing us to measure the relative importance of each attribute in EHC identification.

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“…These advanced algorithms have been widely applied in different knowledge domains [23,24] with very promising results and taking advantage of a consolidated experience. In the field of Civil Engineering, several successful applications of these tools can be found [25][26][27], including solving complex geotechnical problems related to slopes stability assessment [28,29]. These algorithms have also been applied in the study of mechanical properties of soil-binder-water mixtures as reported on Tinoco et al [30], which underline the non-linear learning capabilities of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction

2021

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The reinforcement of stabilized soils with fibers arises as an interesting technique to overcome the two main limitations of the stabilized soils: the weak tensile/flexural strength and the higher brittleness of the behavior. These types of mixtures require extensive laboratory characterization since they entail the study of a great number of parameters, which consumes time and resources. Thus, this work presents an alternative approach to predict the unconfined compressive strength (UCS) and the tensile strength of soil–binder–water mixtures reinforced with short fibers, following a Machine Learning (ML) approach. Four ML algorithms (Artificial Neural Networks, Support Vector Machines, Random Forest and Multiple Regression) are explored for mechanical prediction of reinforced soil–binder–water mixtures with fibers. The proposed models are supported on representative databases with approximately 100 records for each type of test (UCS and splitting tensile strength tests) and on the consideration of sixteen properties of the composite material (soil, fibers and binder). The predictive models provide an accurate estimation (R2 higher than 0.95 for Artificial Neuronal Networks algorithm) of the compressive and the tensile strength of the soil–water–binder–fiber mixtures. Additionally, the results of the proposed models are in line with the main experimental findings, i.e., the great effect of the binder content in compressive and tensile strength, and the significant effect of the type and the fiber properties in the assessment of the tensile strength.

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Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing

Cited by 16 publications

References 38 publications

Evaluating a guest satisfaction model through data mining

Evaluating a guest satisfaction model through data mining

An Evolutionary Neural Network Approach for Slopes Stability Assessment

Soil-Cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction

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