Trends of 4D CAD applications for construction planning

Since the early 1990s, there has been a growing interest in four‐dimensional computer aided design (4D CAD) for construction project planning. Commercial 4D CAD applications are becoming more accessible and the use of this technology allows the construction planner to produce more rigorous schedules. A review of the technical competencies of these packages highlights that most of the commercially available packages concentrate on the use of 4D CAD simulations for aesthetic visualization purposes. Very few packages offer the ability to carry out analytical tasks on the developed simulation and this is often left to the interpretation of the user. A thorough appraisal of emerging research developments in 4D planning highlights that this technology is employed for various applications; however, the amount of detail required in a 4D simulation is still ambiguous. A model is proposed to determine the attributes required for use with each of the various applications of 4D CAD simulations. Finally, various lines of future research are highlighted, including the need for improved use of data exchange standards and the automation of linking the construction tasks to the 3D CAD model.     

Decomposing global solar radiation into its diffuse and direct normal radiation

ABSTRACT

This work presents a model based on Radial Basis Function (RBF) to estimate the diffused solar radiation (DSR) and direct normal radiation (DNR) fractions of solar radiation from global solar radiation in a semiarid area in Algeria based on a database measured between 2013 and 2015. The data has been collected at Applied Research Unit for Renewable Energies, (URAER) at Ghardaia city situated in the south of Algeria. The experimental results show that RBF model estimates DNR and DSR with high performance. The difference between the measured and the predicted values show a normalised Root Mean Square Error (nRMSE) of 0.033 and 0.065 for DNR and DSR, respectively. The obtained values of Determination Coefficient (R²) and Correlation Coefficient (R) are: 97.3%, 98.60%, respectively for DNR and 88.89%, 91.12% For DSR.

However, the obtained results are very plausible and showed that RBF model estimates the DSR and DNR with good accuracy.     

2-D Hydro-Viscoelastic Model for Convective Drying of Highly Deformable Saturated Product

The aim of this work was to simulate in two-dimensions the spatio-temporal evolution of the moisture content, the temperature, and the mechanical stress within a highly deformable and water saturated product during convective drying. The material under study was an elongated potato sample with a square section placed in hot air flow. A comprehensive hydro-thermal model had been merged with a mechanical model, assuming a viscoelastic material, a plane deformation, and an isotropic linear hydric-shrinkage of the sample. This model was validated on the basis of the average water content and core temperature curves for drying trials under different operating conditions. The material viscoelastic properties were measured by means of stress relaxation tests at different water contents. The viscoelastic behavior was described by a generalized Maxwell model whose parameters were correlated to water content. The simulations of the spatio-temporal distributions of mechanical stress were performed and interpreted in terms of product potential damage. The sample shape was also predicted all aver the drying process with reasonable accuracy.     

Single and two-phase flows in a horizontal pipe with a Kenics static mixer: Effect of pressure drop on mixing

Single and two-phase flows pressure drops through a Kenics static mixer were investigated, for liquid and gas Reynolds numbers ranging from 8110 < Re_L < 18 940 to 1730 < Re_G < 8680, respectively. New friction factor correlations were established for single and two-phase flows, showing better agreement than those available in the literature. Dissipated energy and characteristic time constants were estimated from experimental data. For instance, a dissipated energy with a maximum value of 510 W/kg was calculated in two-phase flow with the drift-flux model. The dispersed phase reduced the characteristic mixing times and its influence was more important than the continuous phase for all the characteristic mixing time investigated. Furthermore, the macroscopic characteristic mixing time was shown to be the governing mixing process for almost all gas and liquid flow rates explored.     

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.     

Q2ABR: QoE‐aware adaptive video bit rate solution

In this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.     

Risk-aware business process management using multi-view modeling: method and tool

Requirements Engineering - Risk-aware Business Process Management (R-BPM) has been addressed in research since more than a decade. However, the integration of the two independent research streams...     

Theoretical study of hydrogen desorption on Mg50Ni50 using statistical physics treatment

In this work, Mg₅₀Ni₅₀ hydrogen storage alloy was synthesized by mechanical alloying technique, by using a planetary high energy ball mill (Retsch PM 200). The morphological and microstructural characterization of the powders was performed via scanning electron microscopy and X-ray diffraction. The dehydriding characterization of the composite was performed via a solid gas reaction method at different temperature 313 K, 333 K and 353 K. A new model has been developed, describing pressure-composition isotherms basing on statistical physics treatment. The monolayer model with two types of sites is found to fit very well with experimental data obtained on Mg₅₀Ni₅₀. The parameters involved in the model were determined directly from the experimental data by numerical simulation. The behaviors of these parameters are discussed in relationship with absorption and desorption process. Finally, the different thermodynamical potential functions are derived by statistical physics calculations from the adopted model.