Shake table testing of standard cold-formed steel storage rack

Full‐scale shake table investigations are strongly required to understand the actual performance of storage racks and to improve the rack design guidelines. This paper presents the results of full‐scale shake table tests on New Zealand standard storage rack frames with two‐bay and two‐level to determine the dynamic characteristics of a standard rack structure and to measure the damping of the system. The experimental program was conducted in three phases. First, the identification parameters including the natural frequency and damping of the system were determined through a series of preliminary tests. Then, shake table tests were performed to capture the inelastic response of rack frames under low to medium intensities of El‐Centro ground motion. Finally, the shake‐table tests were repeated with scaling down the time domain and broader ranges of ground motion intensities to consider the performance of taller rack systems. In addition, a comprehensive discussion on the damping of the system is also provided based on the test results. The performance of the rack frame is described through an extensive set of measurements, including rack displacement, pallet sliding, the acceleration of a concrete block and rack frame and the damping of the system in the down‐aisle direction. The results indicate that the standard rack frames are able to endure large inelastic deformations without loss of stability

Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase

Analytical asssessment of the structural behavior of a specific composite floor system at elevated temperatures using a newly developed hybrid intelligence method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures

Utilizing artificial intelligence to predict the superplasticizer demand of self-consolidating concrete incorporating pumice, slag, and fly ash powders

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods

Experimental and numerical investigation of a method for strengthening cold-formed steel profiles in bending

Perforated cold-formed steel (CFS) beams subjected to different bending scenarios should be able to deal with different buckling modes. There is almost no simple way to address this significant concern. This paper investigates the bending capacity and flexural behavior of a novel-designed system using bolt and nut reinforcing system through both experimental and numerical approaches. For the experiential program, a total of eighteen specimens of three types were manufactured: a non-reinforced section, and two sections reinforced along the upright length at 200 mm and 300 mm pitches. Then, monotonic loading was applied to both the minor and major axes of the specimens. The finite element models were also generated and proved the accuracy of the test results. Using the proposed reinforcing system the flexural capacity of the upright sections was improved around either the major axis or minor axis. The 200 mm reinforcement type provided the best performance of the three types. The proposed reinforcing pattern enhanced flexural behavior and constrained irregular buckling and deformation. Thus, the proposed reinforcements can be a very useful and cost-effective method for strengthening all open CFS sections under flexural loading, considering the trade-off between flexural performance and the cost of using the method

PREVALENCE OF SALMONELLASTRAINS ISOLATED FROM INDUSTRIAL QUAIL EGGS AND LOCAL DUCK EGGS, IRAN

Salmonella is a worldwide public health issue as one of the reasons for foodborne illness for humans and animals. Eggs can be a significant source of this bacterium and the prevalence of salmonellosis. Thus, the control of contamination by Salmonella has become essential for the consumer. This study investigates the prevalence, and serotype distribution of Salmonella isolates recovered from industrial quail eggs and local duck eggs collected from Qazvin city, Iran, in 2020. In this cross-sectional study, 130 eggs were collected randomly (including 100 industrial quail eggs and 30 local duck eggs) from the retail and stores in Qazvin city, Iran. Salmonella was isolated from eggshells and egg contents using conventional culture methods for selective isolation of Salmonella and biochemical identification, suspect colonies confirmed by Real-Time PCR assay for the amplification and detection of Salmonella using specific primers. A 16.67% prevalence of Salmonella was observed from duck eggs; however, no Salmonella recovered from quail eggs. Salmonella was isolated from 0% (0 groups of 6 groups) and 16.67% (1 group of 6 groups) of eggshells and contents of duck eggs, respectively. Isolates from positive egg samples characterized as S. Typhimurium. Although Salmonella infection was low in this study, Continuous monitoring is required to prevent health hazards associated with poultry products in this area, and the presence of duck eggs can be a public health problem. The results of this study are essential for the government

The Prevalence of Salmonella Enteritidis in Packaged and Tray Eggs Samples, Qazvin, Iran

Salmonella serotypes are considered as one of the most important foodborne pathogens. Eggs are a main source of the contamination caused by these pathogens and diseases in humans and the prevalence of the salmonellosis. This study was aimed to isolate Salmonella enteritidis from industrial eggs collected from different areas of Qazvin city, Iran in the year 2020.In this cross-sectional study, 200 eggs were collected randomly (including 100 industrial packaged eggs and 100 industrial tray eggs) from the retail and stores located in Qazvin city, Iran. After culturing of eggshells and egg contents according to the classic methods, suspected colonies were confirmed by PCR assay. Salmonella was detected in 10% (4/40) among the egg samples. Salmonella was isolated from 0% (0/40) and 10% (4/40) of eggshells and egg contents, respectively. Isolates from positive egg samples were characterized as S. Typhimurium.Salmonella Typhimurium is the most prevalent serotype of egg contamination in Qazvin city, Iran. It can be regarded as the risk evaluation of possible human foodborne diseases associated with the consumption of contaminated eggs

Investigation of a method for strengthening perforated cold-formed steel profiles under compression loads

Cold-formed steel (CFS) storage rack structures are extensively used in various industries to store products in safe and secure warehouses before distribution to the market. Thin-walled open profiles that are typically used in storage rack structures are prone to loss of stability due to different buckling modes such as local, distortional, torsional and flexural, or any interaction between these modes. In this paper, an efficient way of increasing ultimate capacity of upright frames under compression load is proposed using bolts and spacers which are added externally to the section with certain pitches along the height. Hereinto, experimental tests on 81 upright frames with different thicknesses and different heights were conducted, and the effect of employing reinforcement strategies was examined through the failure mode and ultimate load results. Non-linear finite element analyses were also performed to investigate the effect of different reinforcement spacing on the upright performance. The results showed that the reinforcement method could restrain upright flange and consequently increase the distortional strength of the upright profiles. This method can also be effective for any other light gauged steel open section with perforation. It was also observed that the reinforcement approach is much more useful for short length upright frames compared to the taller frames

Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results

Examining the Existence of Synthetic Dyes in the Nuts Offered in Marivan County, West of Iran

These days, making use of synthetic dyes in producing and processing food, such as nuts, is enhancing due to customer attention. Considering that the application of this type of dyes is not permitted in accordance with the current regulations of the country, controlling nut products in terms of dye is required and overriding. In this study, sampling was performed randomly in 10 nut distribution centers at one-week intervals. Samples were tested respecting the type of dye. A Thin-Layer Chromatography (TLC) method was applied to identify the types of dye. After conducting tests on different samples of nuts, it was recognized that different synthetic dyes such as tartrazine, quinoline yellow, and Ponceau 4R were used in the sample nuts. Of the 50 samples tested and analyzed, 23 samples (46%) had non-permitted synthetic dye, and 27 samples (54%) had permitted synthetic dye. The frequency distribution of synthetic dye among different nuts was significantly different. Furthermore, consumption of almond is associated with lower risk of permitted and non-permitted synthetic compared to pistachio (p< 0.05). Application of these types of dye, due to their glamorous appearance, will be significantly improved in the future. Therefore, with regard to the high consumption of synthetic dyes in food and their adverse effects on health, it seems that measures like increasing the level of awareness of producers and consumers about the effects of consumption or non-consumption of these compounds as well as continuous monitoring of units by health inspectors are necessary