HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Adaptive neuro-fuzzy inference system-based backcalculation approach to airport pavement structural analysis

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) methodology for the backcalculation of airport flexible pavement layer moduli. The proposed ANFIS-based backcalculation approach employs a hybrid learning procedure to construct a non-linear input-output mapping based on qualitative aspects of human knowledge and pavement engineering experience incorporated in the form of fuzzy if-then rules as well as synthetically generated Finite Element (FE) based pavement modeling solutions in the form of input-output data pairs. The developed neuro-fuzzy backcalculation methodology was evaluated using hypothetical data as well as extensive non-destructive field deflection data acquired from a state-of-the-art full-scale airport pavement test facility. It was shown that the ANFIS based backcalculation approach inherits the fundamental capability of a fuzzy model to especially deal with nonrandom uncertainties, vagueness, and imprecision associated with non-linear inverse analysis of transient pavement surface deflection measurements

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Prediction of Adsorption Isotherm of Methane Gas onto Activated Coconut Shell Charcoal by Neuro-Fuzzy Logic Model

ANG (Adsorption Natural Gas) is a technology in which natural gas is adsorbed bya porous adsorbent material (i.e. activated carbon) at relatively low pressure (3.5 MPa up to 5.0 Mpa) that is applied onANG vehicle. This technology, which the storage vessel is filled with a suitable adsorbent material, will have greater energy density compared to the same storage vessel without the adsorbent when filled to the same pressure. Besides that, natural gas is found as a potentially attractive fuel for vehicle use. It is because it is normally cheaper than diesel and gasoline and the vehicle has a less adverse effect compared to liquid-fuel vehicles which emitting more C02 as well as several other air pollutants. However, natural gas has a problem to be utilized at ambient temperature since it is difficult to liquefy because it comprises mostly of methane gas, which having a very low critical temperature (191 K). Therefore, this project is mainly to predict the adsorption isotherm of methane gas onto Activated Coconut Shell Charcoal using Neuro- Fuzzy Logic design system. From the modeling done, the prediction of adsorption isotherm at three different temperatures (273 K, 303 K and 323 K) for methane gas onto activated coconut shell charcoal was successfully implemented by using Neuro-Fuzzy module system. The software used was Fuzzytech 5.52 Professional Demo software. The reference data used was taken from research done by E.Loren Fuller,Jr entitled Characterization of Porous Carbon Fibre and Related Materials. From the simulation done, the objective of this project has been achieved due to average error between experimental/reference data and Neuro-Fuzzy Logic data gained is less than 3%. The isotherm plotted between Neuro- Fuzzy Logic data and experimental data shown almost similar to each other. It can be concluded that the equilibrium model of adsorption isotherm of methane gas onto activated coconut shell charcoal was fitted with the experimental data at different temperature, which in this case were 273 K, 303 K and 323 K. Therefore, from the model developed, it is easier to predict the adsorption isotherm at other temperature but limited within the temperature range (273 K- 323 K) andpressure up to 760Torr

Predicition of Compressive Strength in Light-Weight Self-Compacting Concrete by ANFIS Analytical Model

© 2015 by B. Vakhshouri, S. Nejadi. Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex behavior of concrete in the recent years. Twenty one laboratory experimental investigations on the mechanical properties of LWSCC; published in recent 12 years have been analyzed in this study. The collected information is used to investigate the relationship between compressive strength, elasticity modulus and splitting tensile strength in LWSCC. Analytically proposed model in ANFIS is verified by multi factor linear regression analysis. Comparing the estimated results, ANFIS analysis gives more compatible results and is preferred to estimate the properties of LWSCC

Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process

Additive manufacturing, also known as three-dimensional printing, is a computer-controlled advanced manufacturing process that produces three-dimensional items by depositing materials directly from a computer-aided design model, usually in layers. Due to its capacity to manufacture complicated objects utilizing a wide range of materials with outstanding mechanical qualities, fused deposition modeling is one of the most commonly used additive manufacturing technologies. For printing high-quality components with appropriate mechanical qualities, such as tensile strength and flexural strength, the selection of adequate processing parameters is critical. Experimentally, the influence of process parameters such as the raster angle, printing orientation, air gap, raster width, and layer height on the tensile strength of fused deposition modeling printed items was examined in this work. Through analysis of variance, the impact of each parameter was measured and rated. The system’s response was predicted using an adaptive neuro-fuzzy technique and an artificial neural network. In Minitab software, the Box-Behnken response surface experimental design was used to generate 46 experimental trials, which were then printed using acrylonitrile butadiene styrene polymer materials on a three-dimensional forge dreamer II fused deposition modelling printing machine. The results revealed that the raster angle, air gap, and raster width had significant impacts on the tensile strength. The adaptive neuro-fuzzy approach and artificial neural network predicted tensile strength accurately with an average percentage error of 0.0163 percent and 1.6437 percent, respectively. According to the findings, the model and experimental data are in good agreement.publishedVersio