Iranian Journal of Science and Technology, Transactions of Civil Engineering最新文献

The utilization of fly ash as a waste product of coal combustion is currently limited to being a supplementary cementitious material. Fly ash integrates well with cement, demonstrating favorable qualities in concrete such as good workability, high ultimate strength, and durability. However, the use of fly ash in very high proportions has not been extensively explored due to its weakness in early strength development in concrete. Methods have been investigated to enhance the early compressive strength and compressive strength of very-high-volume fly ash mortar. This research explores the incorporation of fly ash at a very high percentage (80%), also known as very-high-volume fly ash (VHVFA), into mortar using low-molarity alkali solution and thermal activation. The activation of fly ash is examined through alkali activation, specifically utilizing sodium hydroxide (NaOH) solution, and thermal activation involving temperature and activation time. Pre-activating Class F fly ash for 2 h using a 0.3 M NaOH solution at a temperature of 60 °C increased the early compressive strength (at 7 days) by 35% and the compressive strength at 28 days by 12%. Pre-activation for 30 min at room temperature with a 1 M NaOH activator was able to increase the early compressive strength of VHVFA mortar (at 7 days) by 79% for Class C fly ash and by 43% at 28 days. In this study, it is shown that through the use of low-molarity alkali solution and thermal activation, the compressive strength of VHVFA mortar can be increased whether using Class F fly ash or Class C fly ash.

In an era of intense competition and declining profits, airlines are increasingly focused on understanding and meeting customer needs to enhance service quality and attract more passengers. Previous studies have often treated airline choice as a function of observed variables alone. This research fills the gap by incorporating latent psychological factors using a hybrid modeling approach, providing a more holistic view of the decision-making process. In this study, an attempt was made to examine the personal and travel characteristics of passengers and their impact on airline choice at Mehrabad International Airport, Iran. To identify the structures of latent variables, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used, and they were then presented to a multinomial logit-factor analysis (MNL-FA) hybrid approach as observed variables for the choice model of airline and ticket price. The results of EFA indicated that the number of samples was sufficient for this analysis and FA was appropriate to identify the structure of the factor model. CFA results showed that compatible people paid more attention to airline tangibles and image in airline choice. Also, more extroverted people paid more attention to airline costs and image. Moreover, people with higher conscientiousness paid less attention to airline tangibles in airline choice and the costs associated with an airline were less influential in the choice by these people. Results also indicated that neurotic people were sensitive to airline tangibles, costs and flight planning when choosing an airline, and people who were more eager for new experiences paid more attention to tangibles, image and cost factors when choosing an airline, and were less sensitive to flight planning. MNL-FA hybrid model results showed that monthly family expenses above 250$, morning flight time, self-employment, travel with friends, number of vehicles in family ownership, housekeeper and three or more vehicles in family ownership were among the effective factors affecting the choice of the ticket price of an airline. Furthermore, the personality characteristics of compatibility and conscientiousness had positive effects on the choice of airline and ticket price, respectively.

The reuse of waste materials that are released in large quantities is crucial for environmental health. A reasonable solution is to embed waste materials in concrete, since concrete is the most consumed material after water. In this study, CNC (Computer Numerical Control) milling waste, which is generated in large quantities worldwide, was utilized as steel fiber. The waste was added to cementitious mortar mixtures in three different volume ratios. The mortars were exposed to temperatures of 400 °C, 600 °C, and 800 °C, and their post-temperature properties were investigated. Physical analysis, compressive strength, flexural strength, ultrasonic pulse velocity (UPV), and mass loss experiments were conducted. The CNC milling waste (CNCW) showed excellent compatibility with the mortar structure due to its physical and chemical properties. They maintained this accordance against elevated temperatures and provided superior residual properties. Due to their rough and irregular shape, the waste exhibited crack-resistant behavior and prevented the occurrence of temperature-induced cracks. As the amount of waste used and the level of temperature exposure increased, the contribution of the waste to strength also improved. Compared to the control specimens, the CNCW protected the compressive strength, flexural strength, UPV, and mass loss of the mortars by up to 70.74%, 85.10%, 22.63%, and 14.24%, respectively. Finally, this study has demonstrated that CNCW can be effectively used as steel fiber in mortars and that the incorporation of CNCW enhances the high-temperature resistance of these mortars.

Nowadays, the helipad is considered on the roofs of tall buildings to accommodate air taxi services or evacuation in case of a major fire outbreak. On the other hand, lateral load systems play an essential role in tall buildings. In tall buildings, the reinforced concrete end shear walls connect the ends of two reinforced concrete shear walls in all stories to reduce tensions in the end wings. Accordingly, this research compared two 30-story models of reinforced concrete buildings with helipads on top with and without end shear walls. Helipads in two tall buildings should consider both the essential area for touchdown and liftoff and the area where a helicopter begins to move into forward flight during takeoff final approach and takeoff or hovers or lands (approach maneuver). The mentioned tall buildings were subjected to helipad load. Based on the results, the first period and maximum drift story declined by 52% and 47% in 30-story by end shear walls, respectively. In addition, statistical analysis was used for further investigation. The related results showed that skewness and kurtosis coefficients were reduced by 18% and 70% in the 30-story building with an end shear wall and a helipad. The consequences presented suitable performance end shear walls in the tall reinforced concrete building with end shear walls and helipads for personal transportation.

Emergent vegetation in an open channel is a significant factor in flow resistance and has greater influence on flow characteristics. This paper aims to compare the significance of emergent vegetation under various flow conditions including sub and supercritical flow conditions. The methodology utilizes two types of flow condition (sub and supercritical flow), and each type was further considered on the basis of varying discharge and constant channel bed slope and vice versa. For example, under subcritical flow Froude number was defined as a (Fr_(CD-VS) represent Froude number under constant discharge and varying channel bed slope) and (Fr_(CS-VD) represent Froude number under constant channel bed slope and varying discharge). A similar flow condition was defined under supercritical flow conditions. Various parameters such as backwater rise, hydraulic jump, energy reduction, fluid force (RFI%) and moment index reduction (RMI%) as well as reduction of overflow volume (∆Q%) were investigated. The result has been explained in two phases such as sub and supercritical flow. Under sub-critical flow, an undulated hydraulic jump was observed on the downstream side of the vegetation and a maximum energy reduction was 28% in the case of (Fr_(CS-VD)) and 33.4% in the case (Fr_(CD-VS)). Moreover, in the case of (Fr_(CS-VD)) the maximum value of the RFI% and RMI% increased by increasing the values of (Fr_(CS-VD)). The maximum reduction of overflow volume observed was 72% in the case of (Fr_(CS-VD)). Under supercritical flow, an undulated hydraulic jump was observed on the upstream side of vegetation and energy reduction increases by increasing the values of (Fr_(CS-VD)). The maximum value of the RFI% and RMI% observed was 16.67% in the case of (Fr_(CS-VD)). These results contribute to understanding the complex interactions between vegetation and flow dynamics, with implications for managing and mitigating flood risks in vegetated environments.

In this research, an efficient mix of differential quadrature (DQ), particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) methods has been introduced. First-order shear deformation based governing equations of laminated composite plates have been discretized by implementation of the DQ method. The fiber orientation and CNTs volume fraction coefficient of plate’s layers are considered as design variables of the problem. To maximize buckling load of the plate, particle swarm optimization has been used. To generate a new and efficient velocity vector of PSO, as an optimization solver of this problem, the TLBO technique concepts has been used. Efficiency and accuracy of the proposed solution procedure for the plate have been shown and effects of different parameters such as number of layers, CNTs volume fraction, height to length and length to width ratios, boundary conditions and etc. of the plate on the optimal results are investigated.

Determining the fundamental period is a critical aspect of the analysis and design of structures. Existing literature formulas for evaluating this parameter exhibit a wide range of variations. To tackle this problem, various algorithms and techniques are used to learn patterns and relationships from data, enabling to make more precise predictions. In the present study, a dataset consisting of 162 RC frame-building models was analyzed using ETABS 2016. The fundamental period, a critical output parameter, is predicted using four machine-learning models: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Gradient Boosting Regression (GBR). The performance and accuracy of these models were compared to identify the best-performing model for predicting the fundamental period in structural analysis. The efficiency and precision of the machine learning algorithms were assessed based on the R² and root mean square error (RMSE) values. Among all the models, the GBR exhibited the best performance with an R² (Coefficient of determination) score of 0.9995 and an RMSE of 0.017. These results indicate that the GBR model achieved a high level of accuracy and a low level of prediction error in estimating the fundamental period of the RC frame-building models.