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An interpretable machine learning approach for predicting the capacity and failure mode of reinforced concrete columns
No full textDuring seismic events, reinforced concrete (RC) columns play a crucial role in maintaining buildings’ structural integrity. This motivated engineers and practitioners to search for key parameters that influence the load-carrying capacity and failure mechanisms of such columns. However, the complexity and nonlinearity of seismic effects along with the intricate nature of RC columns as a composite system challenge the capabilities of analytical and empirical approaches to accurately capture the response of RC columns. Subsequently, the present study utilizes Machine Learning (ML) techniques to identify the failure modes and predict the corresponding capacities of RC columns based on both their geometrical and material properties. Decision trees and different ensemble methods were employed to predict both the columns’ failure mode and ultimate capacity. A multivariate dataset consisting of 486 cyclically loaded rectangular and circular columns was used to develop and validate the models. In addition, different embedded variable selection techniques were employed to evaluate the significance of input parameters in predicting the performance of columns. Moreover, partial dependence plots and accumulated local effects were employed to uncover the interrelationships between the input features and the modelled outputs. The developed models yielded an average accuracy of 90% and 95% for predicting the failure mode and ultimate capacity of RC columns, respectively. Given such high accuracy, it can be inferred that, ML techniques have the potential to provide efficient and reliable prediction tools to support seismic design and assessment decisions - mitigating seismic risks and empowering resilience planning in the face of extreme events
Experimental and theoretical insights into the adsorption mechanism of methylene blue on the (002) WO3 surface
No full textThis work investigates the efficiency of green-synthesized WO3 nanoflakes for the removal of methylene blue dye. The synthesis of WO3 nanoflakes using Hyphaene thebaica fruit extract results in a material with a specific surface area of 13 m2/g and an average pore size of 19.3 nm. A combined theoretical and experimental study exhibits a complete understanding of the MB adsorption mechanism onto WO3 nanoflakes. Adsorption studies revealed a maximum methylene blue adsorption capacity of 78.14 mg/g. The pseudo-second-order model was the best to describe the adsorption kinetics with a correlation coefficient (R2) of 0.99, suggesting chemisorption. The intra-particle diffusion study supported a two-stage process involving surface adsorption and intra-particle diffusion. Molecular dynamic simulations confirmes the electrostatic attraction mechanism between MB and the (002) WO3 surface, with the most favorable adsorption energy calculated as -0.68 eV. The electrokinetic study confirmed that the WO3 nanoflakes have a strongly negative zeta potential of -31.5 mV and a uniform particle size of around 510 nm. The analysis of adsorption isotherms exhibits a complex adsorption mechanism between WO3 and MB, involving both electrostatic attraction and physical adsorption. The WO3 nanoflakes maintained 90% of their adsorption efficiency after five cycles, according to the reusability tests
Moral worth
No full textThe concept of moral worth, of being creditworthy for doing the right thing, is often seen as essential feature of a moral theory. It forces us to provide a clear account of the relationship between moral motivation and moral action, raising important questions about the demands that morality makes of us. Work on moral worth has a long lineage, especially in Kantian scholarship. Recent years, however, have seen a more focused interest in the nature of moral worth outside of the Kantian tradition. Indeed, part of this interest stems from a rejection of an orthodox Kantian understanding of what moral worth is. In this article, I chart prominent reasons for rejecting the orthodoxy, and distinguish between two rival camps that have emerged: Right Reasons Accounts and Rightness Accounts. I delineate some of the demands that these accounts must meet, and end by discussing a potential way forward that has emerged via hybrid views and goal-based views that attempt to utilise the most promising features of each
Convolutional neural networks for temperature robust medium chemical concentration detection with micro-ring resonators
No full textAn approach to measuring chemical concentrations using a micro-ring resonator (MRR) is proposed which is robust to thermo-optic noise and spectral shifts caused by temperature changes. The method uses a modified ResNet50 with varied kernel size and achieved a mean-square error (MSE) of 4.548E-4, and performance is compared to other machine learning methods including VGG20 and XGBoost. The model was trained to read the transmission spectra of a slotted MRR etched into heavily doped silicon and output the concentrations of chemicals in the surrounding analyte. The chemicals tested on were water, ethanol, methanol, and propanol, with concentrations ranging from 0-100%, with a dataset containing. This occurs over the mid-infrared wavelengths and within the temperature range of 290-310 K. Transfer learning was also utilized to retrain the models on several other datasets, consisting of 528 transmissions each. These datasets operated over different temperature ranges (310-320), and the other with a different set of chemicals, (water, ethanol, methanol and butanol). Similar results were achieved, with both networks achieving similar MSE. We then perform the same process on another design with the same chemicals, also operating over the infrared range, demonstrating the robustness of the method. All datasets used in the study were obtained through simulation, although we hope to test on real data
Simulation Model for Optimizing Resources among Multiple Concurrent Projects: A Case Study
No full textThis paper presents a simulation model for optimizing the allocation of resources in construction projects. The model was designed to help construction companies manage their resources, particularly heavy equipment, which accounts for a significant amount of the direct cost of any project. The model simulates the different projects considering various factors, including project schedules, equipment costs, and availability, to help program managers make informed decisions about allocating their owned resources across the other projects that are working concurrently. The simulation model provides a more flexible and dynamic plan for equipment management, reducing idle time and rental costs. The model was validated through a case study of a construction company with multiple projects of varying types, sizes, locations, and complexities. The results show that the simulation model achieved a 17.45% decrease in costs and a reduction in idle time, thus improving the operational efficiency of the construction company
Exploring the creative city in post-revolutionary Downtown Cairo: On coworking spaces and neoliberal strategies in New Egypt
No full textThis paper examines the remaking of Downtown Cairo in the post-revolutionary era through real-estate’s adoption of the creative city approach using a neoliberal strategy of adaptive reuse for historical buildings. Through mapping a new narrative of coworking spaces in Downtown Cairo and carrying out spatial and observational analyses of Consoleya coworking space as a case study, the paper situates and theorizes spatial practices of the creative city and explores the subject formation of the entrepreneurial citizen as a neoliberal strategy to pacify Downtown Cairo. The analysis draws on the work of Harvey’s entrepreneurial urbanism, Bourdieu’s social construct of capital, and Foucault’s subject formation to demonstrate coworking spaces’ monetization of Downtown Cairo, generation of class privileges and exercise of control through an entertaining atmosphere of co-creation. The case study epitomizes New Egypt’s neoliberal era of soft control recurring in the ongoing urban regeneration projects of Downtown Cairo
Cognitive Impairment and Non-Communicable Diseases in Egypt’s Aging Population: Insights and Implications from the 2021–2022 Pilot of “A Longitudinal Study of Egyptian Healthy Aging” “AL-SEHA”
No full textAs the global population ages, the prevalence of cognitive impairment among older individuals has been steadily rising. Like many countries, Egypt is grappling with the challenges an aging demographic poses. The global network of longitudinal aging studies, modeled after the US Health and Retirement Study (HRS), includes over 40 countries but lacks representation from the Arab/North African region. The proposed ‘A Longitudinal Study of Egyptian Healthy Aging’ (AL-SEHA) will address this gap by providing data on aging in Egypt, the largest Arab/North African country, shedding light on the intricate relationship between cognitive impairment and non-communicable diseases (NCDs) in Egypt’s aging population between 2021 and 2022. This study took place in five governments in Egypt and recruited 299 participants from a population of 50+. The results of the study are from the pilot stage of the original longitudinal study (AL-SEHA)
Defect Detection and Visualization of Understanding Using Fully Convolutional Data Description Models
No full textRecently, image data-based deep learning models such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Convolutional Auto Encoder (CAE), Variable Auto Encoder (VAE), Fully Convolution Network (FCN) and so on have been applied to defect detection for various kinds of industrial products and materials. For example, after some defect is detected in an inspection process using a CNN model, Gradient-weighted Class Activation Mapping (Grad-CAM) or Occlusion Sensitivity is applied to visualization process of the defect areas. This means that a defect detection process and visualization one have to be separately employed in the production line. In this paper, Fully Convolutional Data Description (FCDD) approach is applied to the defect detection and its concurrent visualization of industrial products and materials. Our developed MATLAB application for building defect detection models has already allowed users to efficiently design, train and test various kinds of models such as an originally designed CNN, transfer learning-based CNN, SVM, CAE, VAE, FCN, and YOLO, however, FCDD has not been supported yet. This paper includes the software development to build FCDD models. The usefulness of FCDD models in terms of defect detection is compared with conventional transfer learning-based CNN models
Agent-Based Modeling for Delay Analysis Claims
No full textDelays in a construction project have been a long-standing dilemma due to their inevitable nature. Consequently, time overrun in construction projects has been the main area of investigation by academic researchers and practitioners alike. When projects become more complex, the accuracy of quantifying delays can be an arduous process; without the proper quantification, this leaves contractors subject to the application of liquidated damages or losses. Various reasons for delays in the construction industry can lead to a ripple effect on certain path(s) of activities which cannot be easily traced throughout the lifetime of mega construction project with interdependent disciplines. The current methods within the delay analysis realm all involve a cumbersome process of data collection regarding the delaying events whether it would be utilized in a retroactive or prospective approach. Additionally, lack of proper documentation and records after the event has taken place will lead to an inaccurate delay analysis causing the upheaval of disputes between parties. Therefore, this paper allots for a real-time recording of delaying events through conducting delay analysis using agent-based modeling. This allows for the effect of delaying events to be instantaneously measured in terms of additional time suffered. Not only so, but agent-based modeling avoids the need for delay analysts to investigate the entirety of affected activities to link to the delaying event. A case study was then applied to a path of activities simulated using AnyLogic to validate the agent-based approach for delay analysis
Fiber Elastomer Modified Asphalt for the Development of Resilient Porous Asphalt Mixtures
No full textHeavy rain is one of the extreme weather events which pose a variety of serious risks on transportation infrastructures. Porous asphalt pavement can be used as a sustainable solution to mitigate the effects of such heavy rains. The objective of this study was to study the potential of using fiber elastomer modifier (FEM) to produce porous asphalt mixtures of high quality and enhanced performance. This was done through an experimental program composed of three different phases. The first phase was the development and the rheological, chemical, and microstructural characterization of the FEM modified asphalt. The second phase focused on using FEM to produce porous asphalt mixtures using different techniques. The third phase was the characterization of the porous asphalt mixtures to study their anticipated performance. The FEM asphalt performance grade, PG (76-22), proved enhanced rheological properties in terms of better rutting resistance indicated by higher G*/sin δ over a wide range of temperatures and lower Jnr3.2 value of about 19% compared to the virgin asphalt and an enhanced fatigue cracking resistance manifested by the significant reduction in the fatigue cracking indicator G* sin δ with about 94%. Finally, porous asphalt mixtures were produced of an enhanced performance based on the dynamic modulus. Higher E* values at higher temperatures/lower frequencies and lower E* values at lower temperatures/higher frequencies were reported for the FEM porous asphalt mixture in reference to the, control dense-graded HMA mixture, promising an enhanced both rutting and fatigue resistances of the produced porous asphalt mixtures