The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Hot-dip galvanized steel dip-coatedwith ureasilicate hybrid in simulated concrete pore solution: Assessment of coating morphology and corrosion protection efficiency

The barrier effect and the performance of an organic–inorganic hybrid (OIH) sol–gel coating are highlydependent on the coating deposition method as well as processing conditions. In this work, studies onthe influence of experimental parameters using the dip coating method were performed. Factors suchas residence time (Rt), a curing step between each dip step and the number of layers of sol–gel OIHfilms deposited on HDGS to prevent corrosion in highly alkaline environments were studied. These OIHcoatings were obtained using a functionalized siloxane, 3-isociantepropyltriethoxysilane that reactedwith a diamino-functionalized oligopolymer (Jeffamine®D-230). The barrier efficiency of OIH coatings insimulated concrete pore solutions (SCPS) was assessed in the first moments of contact, by electrochemicalimpedance spectroscopy and potentiodynamic methods. The durability and stability of the OIH coatings inSCPS was monitored during eight days by macrocell current density. The morphological characterizationof the surface was performed by scanning electronic microscopy before and after exposure to SCPS.Glow discharge optical emission spectroscopy was used to obtain quantitative composition profiles toinvestigate the thickness of the OIH coatings as a function of the number of layers deposited and theinfluence of the Rt in the coating thickness.

Seismic behavior of concrete block masonry buildings

Tese de Doutoramento em Engenharia Civil

Sustainability assessment of envelope wall solutions using MAXergy methodology

Projeto de investigação integrado de International Master in Sustainable Built Environment

Correction of the initial conditions

"Series title: Springerbriefs in applied sciences and technology, ISSN 2191-530X"