Eco-Friendly Rubberized Concrete

Eco-Friendly Rubberized Concrete Sidi Mohammed Khalil CHERIF LOUAZANI, Graduate Research Assistant, Graduate Student in Construction Management, Construction management Department, CoACM Amaal Al Shenawa, Ph. D., Assistant Professor, Construction management Department, CoACM Metin Oguzmert, Ph. D., Associate Professor, Department of Civil and Environmental Engineering, SPCEET Tire waste (rubber) causes serious environmental issues because of the rapid rise in and numerous variations of modern developments worldwide. According to the U.S. EPA, 9.16 million tons of rubber and leather was generated in the U.S. in 2018, more than 50% ends in the landfill, while just 1.67 million tons, or about 18 % was recycled. With recycling rates in the U.S. remaining low, there is a strong need to find other ways to keep rubber waste out of the landfill. Using rubber waste in concrete not only conserves raw materials but also provides an alternative to landfilling or burning, the latter of which increases CO2 emissions and releases hazardous gases. Using recycled rubber aggregate lightens concrete, increases its workability, and improves its ductility. In this research, tire waste (recycled rubber) aggregate used to replace fine aggregate in concrete mixture up to 40%. Its impact on the physical and mechanical properties of concrete was examined. The experimental results showed improvement in the workability and unit weight while dropping in the compressive strength with increasing replacement ratios

Cost-Efficient Bridge Scour Health Monitoring using Commercial Sensors

Bridge scouring has been a major international issue regarding bridge health and the overall longevity of a bridge. A common bridge health concern such as scouring accounts for close to 60% of bridge failures in the United States and is a leading cause to a bridge being in critical condition. Traditional methods to combat this failure is to measure the scour depth to assess a bridge health. Due to safety concerns of the traditional method, this study proposes to monitor a bridge’s health using a vibration-based technique. At present, vibration-based techniques have yet to be utilized reliably in the field. The sensor system chosen for this study is the accelerometers. Acceleration data collected from the sensors can be translated into frequency and amplitudes to monitor bridge health status. A laboratory experiment is conducted within this study with an oscillating platform to simulate expected vibrations that would be seen within the field. Once laboratory verifications were done, the sensor system will be deployed in the field for further observations. Collected data from this study is expected to show distinction between oscillation behavior of a scour critical bridge and non-scour critical bridge when compared to the theoretical natural vibration of a bridge. The laboratory and field collected data from this study will be discussed in the symposium

Displacement-based seismic design of structures using equivalent linear system parameters

This study presents a new method to calculate equivalent linear system parameters for estimating the maximum inelastic seismic response of structures. Since inelastic response is dependent on the type of hysteretic model used, two hysteretic models: the Bilinear and Q-model hysteretic models, are considered. The effects of these hysteretic models, initial damping, post yielding stiffness and degrading behavior on the equivalent linear system\u27s period and equivalent viscous damping are investigated. To check the accuracy of the proposed method, the maximum inelastic responses of structures computed using six different methods are compared and studied. It is observed that the proposed approach gives more accurate results than the other methods for structures that experience ductile flexural behavior with natural periods in the range 0.3 to 3 seconds. The proposed equivalent period and equivalent viscous damping equations are then implemented into the Displacement Based Design Method to design sixteen single degree-of-freedom sample structures. Dynamic inelastic time history analyses are performed on all these sample structures to evaluate the accuracy of the proposed seismic design approach. The results indicate that the accuracy and versatility of the Displacement Based Design Method can be improved by the use of the proposed equivalent linear system parameters

Estimation of the Bond Strength of Fiber-Reinforced Polymer Bars in Concrete Using Artificial Intelligence Systems

Fiber-reinforced polymer (FRP) bars have recently been introduced to the market as an alternative to steel for internal reinforcement for concrete construction exposed to situations that could cause corrosion. The bond behavior of FRP bars varies from that of steel bars, mostly due to variations in material properties and surface textures. Because of the unexpected nature of the crucial FRP–concrete interfacial (FCI) bond strength, the bond strength between FRP bars and concrete cannot be exactly determined. Numerous experimental investigations have been conducted with related empirical models established in an attempt to resolve this problem. These models were found to have a restricted capacity for generalization due to the small sample sizes of the experiments. Therefore, a more powerful numerical technique capable of processing large data sets with all possible parameters that may affect the relationship and considering the nonlinearity of data tendency is needed. In this study, the artificial neural networks technique and adaptive neuro-fuzzy inference system were utilized to predict the FRP–concrete bond behavior based on 238 data points collected from different studies in the literature. The performance of the ANN and ANFIS models in predicting the bonding strength was compared to other models published in the literature and codes. The results showed that the ANN and ANFIS models gave higher prediction performance than other models, with a slight advantage for the ANN model. For instance, the R-squared values of the proposed ANN and ANFIS were 0.94 and 0.92, respectively, for 20 data points that were not used to develop the ANN and ANFIS models. Based on the sensitivity analysis, the FRP diameter and compressive strength of concrete were found to be the most effective parameters on the bond strength in both the ANN and ANFIS models. In contrast, the bar position and surface texture had a lower importance index