The Study of mechanical and electronic installation requirements based on passive defense approach (Case study: Tabriz Central Library)

Passive defense is defined as a series of measures in order to reduce civilian population, installation and structures vulnerability against explosive loading and probable threats. Hazardous installations can lead to secondary explosions and threats (Iran's National Building Regulations Office, 2013). In addition, temporary and permanent deficiencies can stop our activities. In this study, in the first place, electronic and mechanical installation requirements in passive defense are introduced. Then, the amount of these requirements observance is investigated at Tabriz Central Library as one of the most important structures located in Tabriz. The results indicate that the passive defense requirements are not obeyed at Tabriz Central University. Therefore, the clientele, staff and documents are in danger of damage and destruction

Analysis of Civil, Architectural and Urban Planning of Passive Defense: A Case Study in Central Library of Tabriz

Passive defense is a set of unarmed actions for reducing the vulnerability of human resources, facilities and structures in encountering to the explosions and possible threats.  Disregarding the necessities of passive defense can lead to the loss of human lives, intensification of the damages to the structures and consequently the consumption of large amounts of finance in facing the possible threats. Therefore, considering these necessities has a special significance. In this study, the amount of observation of these necessities in the central library of Tabriz was analyzed as one of the important structures of the Tabriz city. The results of this study indicate that even by considering the importance of this center, the passive defense necessities in this center have not been observed neither from the structural point of view nor the architecture and nor the foundations. Therefore, the structure of this center, the staff and the people who go there are constant subjects of damage and threat

Prediction of scour depth at breakwaters due to non-breaking waves using machine learning approaches

Coastal structures may cease to function properly due to seabed scouring. Hence, prediction of the maximum scour depth is of great importance for the protection of these structures. Since scour is the result of a complicated interaction between structure, sediment, and incoming waves, empirical equations are notas accurate as machine learning schemes, which are being widely employed for the coastal engineering modeling. In this paper, which can be regarded as an extension of Pourzangbar et al. (2016), two soft computing methods, a support vector regression (SVR), and a model tree algorithm (M5'), have been implemented to predict the maximum scour depth due to non-breaking waves. The models predict therelative scour depth (Smax/H0) on the basis of the following variables: relative water depth at the toe ofthe breakwater (htoe/L0), Shields parameter (theta), non-breaking wave steepness (H0/L0), and reflection coef-ficient (Cr). 95 laboratory data points, extracted from dedicated experimental studies, have been used for developing the models, whose performances have been assessed on the basis of statistical parameters.The results suggest that all of the developed models predict the maximum scour depth with high preci-sion, the M5model performed marginally better than the SVR model and also allowed to define a set oftransparent and physically sound relationships. Such relationships, which are in good agreement withthe existing empirical findings, show that the relative scour depth is mainly affected by wave reflection

Data-driven and numerical approaches to predict thermal comfort in traditional courtyards

This paper studies the climactic performance of the 10 traditional courtyards located in warm-dry climates of Kashan and cold climates of Ardabil based on shading and sunlit coverage. The modelling process comprises two sections: first, a number of numerical simulations are run using Envi-met software to detail the shading and sunlit percentage, PET and PMV in the samples of interest. These numerical models are validated on the basis of the results made available by field observations. Such validation revealed an excellent agreement between the numerical solution and the benchmarking data. Afterwards, GP is used to evolve some equations for predicting PET and PMV using the data points derived from the numerical simulations. The results suggest that regarding the thermal indices (PET and PMV), there is a high correlation between the shadow and sunlit effects and thermal comfort in Kashan's houses in comparison with Ardabil houses. However, in tropical regions (Kashan), summer shading and winter sunlit have a greater effect on thermal comfort and temperature adjustment than cold regions. Moreover, the statistical criterion, as well as reliability analysis and contour plots show that the GP developed formulas can be exploited in predicting the PET and PMV based on the shading percentage

Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks

International audienceScour may act as a threat to coastal structures stability and reduce their functionality. Thus, protection against scour can guarantee these structures’ intended performance, which can be achieved by the accurate prediction of the maximum scour depth. Since the hydrodynamics of scour is very complex, existing formulas cannot produce good predictions. Therefore, in this paper, Genetic Programming (GP) and Artificial Neural Networks (ANNs) have been used to predict the maximum scour depth at breakwaters due to non-breaking waves (Smax/Hnb). The models have been built using the relative water depth at the toe (htoe/Lnb), the Shields parameter (θ), the non-breaking wave steepness (Hnb/Lnb), and the reflection coefficient (Cr), where in the case of irregular waves, Hnb=Hrms, Tnb=Tpeak and Lnb is the wavelength associated with the peak period (Lnb=Lp). 95 experimental datasets gathered from published literature on small-scale experiments have been used to develop the GP and ANNs models. The results indicate that the developed models perform significantly better than the empirical formulas derived from the mentioned experiments. The GP model is to be preferred, because it performed marginally better than the ANNs model and also produced an accur