Behzad Behnia

The construction of tall buildings generates a high spatial and temporal concentration of greenhouse gas (GHG) emissions. Research studies have shown that as building height increases, more resources per floor area are required to withstand the increasing effects of lateral loads (wind and earthquake). This has major implications for the environmental performance of tall buildings since the Embodied GHG Emissions (EGHGE) of structural systems tend to represent the greatest portion of the life cycle GHG emissions of tall buildings. This study presents a data driven-based approach for decarbonization of tall buildings and evaluates the significant impact of material types (concrete, steel, and timber), concrete strength, and structural systems relative to a building's height on the EGHGE associated with tall buildings. In mitigating the effects of climate change, this research implements machine learning (ML) algorithms as an early-stage design tool to facilitate the choice of materials, and structural systems for tall buildings. This work considers a wide range of high-rise buildings with four different types of lateral structural systems (Braced-Frame system, Outrigger-Belt system, Shear Wall system, and Tubular system), four different types of construction materials (concrete, steel, hybrid, and timber), varying heights (ranging from 10 to 100 stories), and various concrete materials with different compressive strength ranging from 32 to 90 MPa. Data gathered from more than 100 existing tall building projects along with the data obtained from finite element analysis of 120 high-rise buildings models were utilized to train various ML regression algorithms: Decision Tree, Support Vector Machine, Polynomial Regression, and Elastic-Net Regularized Regression. The performance of ML models was carefully assessed, and the best prediction model was selected to estimate the total amount of CO2 emissions for high-rise buildings. Results indicate that hybrid structures with the Out-Rigger-Belt system exhibit the lowest carbon emission (110 kg/m2) compared to other structural types and systems.

A novel approach for the fracture characterization of soft adhesive materials using spiral cracking patterns is presented in this study. This research particularly focuses on hydrocarbon polymeric materials, such as asphalt binders. Ten different asphalt materials with distinct fracture characteristics were investigated. An innovative integrated experimental–computational framework coupling acoustic emissions (AE) approach in conjunction with a machine learning-based Digital Image Analysis (DIA) method was employed to precisely determine the crack geometry and characterize the material fracture behavior. Cylindrical-shaped samples (25 mm in diameter and 20 mm in height) bonded to a rigid substrate were employed as the testing specimens. A cooling rate of −1 °C/min was applied to produce the spiral cracks. Various image processing techniques and machine learning algorithms such as Convolutional Neural Networks (CNNs) and regression were utilized in the DIA to automatically analyze the spiral patterns. A new parameter, “Spiral Cracking Energy (E_Spiral)”, was introduced to assess the fracture performance of soft adhesives. The compact tension (CT) test was conducted at −20 °C with a loading rate of 0.2 mm/min to determine the material’s fracture energy (G_f). The embrittlement temperature (T_EMB) of the material was measured by performing an AE test. This study explored the relationship between the spiral tightness parameter (“b”), E_Spiral, G_f, and T_EMB of the material. The findings of this study showed a strong positive correlation between the E_Spiral and fracture energies of the asphalt materials. Furthermore, the results indicated that both the spiral tightness parameter (“b”) and the embrittlement temperature (T_EMB) were negatively correlated with the E_Spiral and G_f parameters.

A transformative and radically different approach for fracture characterization of soft adhesive materials through use of spiral cracking patterns is presented. This research particularly focuses on hydrocarbon polymeric materials such as asphalt binders. Five different asphalt materials were utilized in this study. An innovative integrated experimental-computational framework coupling Acoustic Emissions (AE) approach along with machine learning-based Digital Image Analysis (DIA) method is employed to determine the crack geometry and to accurately characterize fracture behavior of the material. Various image processing and machine learning techniques such as Convolutional Neural Networks (CNN), skeletonization, segmentation, and regression were used in DIA to automatically analyze spiral patterns. A new parameter called the “Spiral Cracking Energy (E_Spiral)” to assess fracture performance of soft adhesives is introduced in this work. This study also explores the relationship between E_Spiral and fracture energy of the material.

This chapter focuses on various applications of acoustic emissions (AE) technique in evaluation of cracking in asphalt pavements including (1) assessment of low-temperature cracking of asphalt binders and mixtures and (2) quantitative characterization of rejuvenators’ efficiency in restoring aged asphalt pavements to their crack-resistant state. The AE-based embrittlement temperature results of 24 different asphalt materials consisting of eight different binders, each at three oxidative aging levels are presented. Results show that embrittlement temperatures correlated well with corresponding bending beam rheometer (BBR-based) critical cracking temperatures with R2 = 0.85. This chapter also presents application of AE for evaluation of rejuvenators’ efficiency on asphalt materials at various oxidative aging levels. The Geiger’s iterative source location method was employed to accurately determine embrittlement temperatures throughout the thickness of rejuvenator-treated asphalt samples. Results showed that the low temperature cracking properties of oxidative aged materials after 2 weeks of dwell time of rejuvenator have been recuperated. Moreover, it was observed that cracking characteristics of aged asphalt 6–8 weeks after applying rejuvenator far exceeded that of the virgin materials. The promising results suggest that the AE technique can be considered as a viable approach for the assessment of low temperature behavior of asphalt pavements.

InP-based 4×600 Gb/s capable coherent transmitter and receiver assemblies with hybrid integrated SiGe drivers operate at 66GBd, 64QAM over a 200km link. Additionally, a fully packaged pair of modules with real-time DSP ASIC operates error free at 600Gb/s with 69GBd, DP-64QAM.

Low-temperature cracking is a major form of distress that can compromise the structural integrity of asphalt pavements located in cold regions. A review of an Acoustic Emission (AE)-based approach is presented that is capable of assessing the low-temperature cracking performance of asphalt binders and asphalt pavement materials through determining their embrittlement temperatures. A review of the background and fundamental aspects of the AE-based approach with a brief overview of its application to estimate low-temperature performance of unaged, short-term, and long-term aged binders, as well as asphalt materials, is presented. The application of asphalt pavements containing recycled asphalt pavement (RAP) and recycled asphalt shingles (RAS) materials to thermal cracking assessment is also presented and discussed. Using the Felicity effect, the approach is capable of evaluating the self-healing characteristics of asphalt pavements and the effect of cooling cycles upon their fracture behavior. Using an iterative AE source location technique, the approach is also used to evaluate the efficiency of rejuvenators, which can restore aged asphalt pavements to their original crack-resistant state. Results indicate that AE allows for relatively rapid and inexpensive characterization of pavement materials and can be used towards enhancing pavement sustainability and resiliency to thermal loading.