The execution of public sector construction projects often requires the use of financial resources not foreseen during the tendering phase, which causes management problems. This study aims to present a computational model based on artificial intelligence, specifically on artificial neural networks, capable of forecasting the execution cost of construction projects for Brazilian educational public buildings. The database used in the training and testing of the neural model was obtained from the online system of the Ministry of Education. The neural network used was a multilayer perceptron as a backpropagation algorithm optimized through the gradient descent method. To evaluate the obtained results, the mean absolute percentage errors and the Pearson correlation coefficients were calculated. Some hypothesis tests were also carried out in order to verify the existence of significant differences between real values and those obtained by the neural network. The average percentage errors between predicted and actual values varied between 5% and 9%, and the correlation values reached 0,99. The results demonstrated that it is possible to use artificial intelligence as an auxiliary mechanism to plan construction projects, especially in the public sector.

In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values ​​were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.

Facade maintenance actions are driven by results obtained in the inspection phase. Some methodological proposals aimedat optimizing the inspection process have been discussed, notablydigital image processing (DIP) techniques associated with unmanned aerial vehicle (UAV) imagery. Using UAV speeds up the access to the inspected area, and DIP techniques help to automate the identification of pathological manifestations. This article aims to apply DIP techniques to detect areas where the ceramic cladding on building facades is detaching. The methodology referred to herein starts with the creation of a database (images) captured by cell phone and UAV. The object detection algorithm YOLO (You Only Look Once) was applied to the database images. The results indicated these techniques are very promising, with a 94% precision level in the tests performed. The precision index obtained indicates that the model is applicable in practice and discussions about its limitationshelp improve the proposed methodology.

A epilepsia é uma doença neurológica crônica, que aumenta a predisposição de umindividuo sofrer convulsões recorrentes. Pessoas que sofrem de epilepsia poderiam viver livres de convulsões, desde que fossem diagnosticadas previamente ou recebessem um tratamento adequado. Portanto metodologias que simplifiquem e agilizem o diagnóstico e o tratamento destes indivı́duos são válidas e necessárias. Esse trabalho tem por objetivo o apresentação de uma metodologia de identificação de janelas de eletroencefalogramas (EEG's) com presença ou ausência de crises epilépticas. Essa metodologia é baseada no cálculo de métricas que dependem dos momentos estatı́sticos de segunda, terceira e quarta ordem. Os vetores de características obtidos a partir dos momentos estatı́sticos, a segunda com a rotação dos mesmo vetores, utilizando a análise de componentes principais (PCA). Foram extraı́das janelas de 1 segundo de arquivos da base de dados CHB-MIT. O modelo de classificação proposto obteve para os vetores de caracterı́sticas e para as componentes oriundas do PCA, acurácia de 86.4% e 94.6%, respectivamente. Acreditasse que com devidas adaptações melhorias o modelo pode ser embarcado em um dispositivo, para classificação de janelas em tempo real.

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Introduction: Classifying multiple cardiac signals simultaneously is still a challenge for computational methods. We propose a computational algorithm that performs a multi-class classification. We use a combination of global features of the electrocardiogram (ECG) signal, where we statistically analyze the complete signal, as well as include the local features of the cardiac cycles. Methodology: 286 ECG signals obtained from the MIT-BIH Database were evaluated:18 healthy, 48 with arrhythmia, 23 with Atrial Fibrillation, 70 with apnea, 78 with supraventricular arrhythmias, 15 with congestive heart failure, 11 with epilepsy, 5 with fetal electrocardiogram, 18 with ventricular tachyarrhythmia. For each of the 286 signals, 1200 cardiac cycles from a total of one second measurement were used, characterized by 400 milliseconds before and 600 milliseconds after the R wave. We calculated variance, skewness and kurtosis from the 286 signals, extracting 1200 features from the cardiac cycles for each of the ECG signals. These hyperparameters were used as input for the Radial Basis Function Neural Networks (RBF), where 80% were for training and 20% for testing, with cross-validation of K-fold = 10. The RBF had one hidden layer with error correction by mean squared error, number of iterations equal to 1000, number of neurons equal to 25 and learning rate 0.01. Results: Each group of cardiac signals evaluated in this study had its own features and, therefore, had unique parameters for each of the different pathologies in a three dimensional plot, as illustrated in the figure. The average accuracy of the proposed method for multi-class classification of cardiac signals was 99.98 %. Conclusion: The high success rate in the multi-class classification for eleven classes of cardiac signals makes the proposed methodology an promising alternative to aid in the diagnosis of cardiac pathologies.