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.

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.

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.

Os edifícios frequentemente apresentam elementos que representam desafios significativos para inspeção e manutenção, como a fachada. O uso de Veículos Aéreos Não Tripulados (VANTs) tem contribuído para esse tipo de inspeção, no entanto, ainda há espaço para melhorias. O objetivo deste artigo é apresentar um algoritmo computacional capaz de quantificar a falta de revestimentos cerâmicos em fachadas de edifícios utilizando imagens capturadas por VANTs e técnicas de processamento digital de imagens (PDI) . A metodologia utilizada foi composta por quatro fases: a construção de um conjunto de dados, o pré-processamento das imagens, a aplicação do algoritmo YOLO v4 para a identificação das áreas com revestimento cerâmico e a elaboração de um algoritmo para a contagem do número de revestimentos cerâmicos faltantes nas imagens da fachada. Os resultados da quantificação demonstraram que o erro médio total do algoritmo varia entre 0,3 e 1,5 placas cerâmicas quando aplicado a um grupo de 53 novas imagens e que o tempo de identificação e quantificação das placas cerâmicas faltantes reduzem drasticamente quando o algoritmo proposto é utilizado.

Hypsarrhythmia is an electroencephalographic pattern specific to some epileptic syndromes that affect children under one year of age. The identification of this pattern, in some cases, causes disagreements between experts, which is worrisome since an inaccurate diagnosis can bring complications to the infant. Despite the difficulties in visually identifying hypsarrhythmia, options of computerized assistance are scarce. Aiming to collaborate with the recognition of this electropathological pattern, we propose in this paper a mathematical index that can help electroencephalography experts to identify hypsarrhythmia. We performed hypothesis tests that indicated significant differences in the groups under analysis, where the p-values were found to be extremely small.