California bearing ratio tests of enzyme-treated sedimentary residual soil show no improvement

Environmental concerns have significantly influenced the construction industry regarding the identification and use of environmentally sustainable construction materials. In this context, enzymes (organic materials) have been introduced recently for ground improvement projects such as pavements and embankments. The present experimental study was carried out in order to evaluate the compressive strength of a sedimentary residual soil treated with three different types of enzymes, as assessed through a California bearing ratio (CBR) test. Controlled untreated and treated soil samples containing four dosages (the recommended dose and two, five and 10 times the recommended dose) were prepared, sealed and cured for four months. Following the curing period, samples were soaked in water for four days before the CBR tests were administered. These tests showed no improvement in the soil is compressive strength; in other words, samples prepared even at higher dosages did not exhibit any improvement. Nuclear magnetic resonance (NMR) spectroscopy tests were carried out on three enzymes in order to study the functional groups present in them. Furthermore, X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) tests were executed for untreated and treated soil samples to determine if any chemical reaction took place between the soil and the enzymes. Neither of the tests (XRD nor FESEM) revealed any change. In fact, the XRD patterns and FESEM images for untreated and treated soil samples were indistinguishable

Teacher Reflectivity Revisited: Is Teaching Reflectivity Gaining a Foothold in Iran?

Given that reflectivity could help keep the teaching profession vibrant and responsive, reflective teaching practice has become an essential component of teacher education. In recent years, some efforts have been underway to implement it in our educational system, in general and in language teaching, in particular. The present study aimed to investigate the extent to which Iranian English language teachers are reflective, if at all. To this end, a five-point Likert-scale questionnaire including 26 items, originally developed by Akbari et al. (2010) and validated for the purposes of the current study, was used. The participants of the study comprised 217 practicing EFL teachers selected through random sampling. Data analysis, conducted through descriptive statistics, revealed that Iranian English language teachers are reflective in all dimensions of reflection, though degree of reflectivity varies across these dimensions. This finding is promising and suggests that reflectivity is gaining a foothold in our language education

Examination of the Behavior of Gravity Quay Wall against Liquefaction under the Effect of Wall Width and Soil Improvement

Deformation of quay walls is one of the main sources of damage to port facility while liquefaction of backfill and base soil of the wall are the main reasons for failures of quay walls. During earthquakes, the most susceptible materials for liquefaction in seashore regions are loose saturated sand. In this study, effects of enhancing the wall width and the soil improvement on the behavior of gravity quay walls are examined in order to obtain the optimum improved region. The FLAC 2D software was used for analyzing and modeling progressed models of soil and loading under difference conditions. Also, the behavior of liquefiable soil is simulated by the use of “Finn” constitutive model in the analysis models. The “Finn” constitutive model is especially created to determine liquefaction phenomena and excess pore pressure generation

Stabilization expansive clayey with nano-lime to reduce environmental impact

For years, geotechnical engineers have been concerned about expansive soils. Expansive soils are characterized by large volumetric changes related to variations in moisture content. Variations in soil water content may take place naturally during seasonal changes or maybe manmade caused by dewatering activities. The quantity of shrinkage and swell is influenced by numerous parameters, including the quantity of minerals clay in the soil, moisture content, dry density, and climate change. In most countries, numerous structures, including pavements and buildings, are damaged as a result of this shrinkage/swelling. Several ground improvement techniques are available for stabilizing expansive soil to modify its engineering performance. These methods include soil replacement, mixing with chemical additives, and soil reinforcement. The present study expressions the effect of nano-lime (i.e., 0.1, 0.3, 0.5, 0.7, 1.0, 2.0 and 3.0%), and lime (1, 3, 5, 8, and 10%), as chemical additive to improve clayey soil (i.e., illite and kaolinite). The effect of nano-lime and lime were investigated using Atterberg’s limits tests. The Atterberg limits were screening significant changes in the proportion of additional nano-lime and lime. The results show that less amount of nano-lime (1% and 2% for illite and kaolinite respectively) decreased the plastic limit, while for lime it was reported 8% for illite and 5% for kaolinite respectively. In conclusion, less quantity of nano-lime (1-2%) is able to improve soil parameters

Potential of Using Nanocarbons to Stabilize Weak Soils

Soil stabilization, using a variety of stabilizers, is a common method used by engineers and designers to enhance the properties of soil. The use of nanomaterials for soil stabilization is one of the most active research areas that also encompass a number of disciplines, including civil engineering and construction materials. Soils improved by nanomaterials could provide a novel, smart, and eco- and environment-friendly construction material for sustainability. In this case, carbon nanomaterials (CNMs) have become candidates for numerous applications in civil engineering. The main objective of this paper is to explore improvements in the physical properties of UKM residual soil using small amounts (0.05, 0.075, 0.1, and 0.2%) of nanocarbons, that is, carbon nanotube (multiwall carbon nanotube (MWCNTs)) and carbon nanofibers (CNFs). The parameters investigated in this study include Atterberg’s limits, optimum water content, maximum dry density, specific gravity, pH, and hydraulic conductivity. Nanocarbons increased the pH values from 3.93 to 4.16. Furthermore, the hydraulic conductivity values of the stabilized fine-grained soil samples containing MWCNTs decreased from 2.16E-09 m/s to 9.46E-10 m/s and, in the reinforcement sample by CNFs, the hydraulic conductivity value decreased to 7.44E-10 m/s. Small amount of nanocarbons (MWCNTs and CNFs) decreased the optimum moisture content, increased maximum dry density, reduced the plasticity index, and also had a significant effect on its hydraulic conductivity

Higher desirability in solving multiple response optimization problems with committee machine

Many industrial problems need to be optimized several responses simultaneously. These problems are named multiple response optimization (MRO) and they can have different objectives such as Target, Minimization or Maximization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. In addition, optimization usually is done on Global Desirability (GD) function. Current article is a development for recent authors' work to determine economic run number for application of CM and GA in MRO problem solving. This study includes a committee machine with four different ANNs. The CM weights are determined with GA which its fitness function is minimizing the RMSE. Then, another GA specifies the final solution with object maximizing the global desirability. This algorithm was implemented on five case studies and the results represent the algorithm can get higher global desirability by repeating the runs and economic run number (ERN) depends on the MRO problem objective. ERN is ten for objective “Target”. This number for objectives which are mixture of minimization and maximization ERN is five. The repetition are continued until these ERN values have considerable increased in maximum GD with respect to average value of GD. More repetition from these ERN to forty five numbers cause a slight raise in maximum GD

Addiction and the Risk of Common Bile Duct Stones: A 4-Year Retrospective Population-Based Study in Mashhad, Iran

Background: As a common digestive disorder, choledocholithiasis can have serious consequences, including death. Given that opioids have been shown to contribute to the spasm of Oddi’s sphincter, which results in biliary stasis in the common bile duct (CBD), it is likely that opioids can also raise the prevalence of choledocholithiasis. In this regard, this study aimed to investigate how common opium addiction was among choledocholithiasis patients in Mashhad, Iran.Methods: The current retrospective observational study was conducted on 599 patients with choledocholithiasis who underwent endoscopic retrograde cholangiopancreatography (ERCP), utilizing information gathered at the Ghaem hospital in Mashhad, Iran, between 2011 and 2015. Patient data were collected from files and records using certain criteria such as gender, opium addiction, hepatic enzymes (AST, ALT, ALP), plasma levels of total bilirubin, and direct bilirubin. The size of the CBD stones as well as the correlation between the gallbladder and CBD stones were calculated.Findings: From among 599 patients included, 345 (57.6%) were female and 254 (42.4%) were male. Moreover, 195 patients (32.2%) had opiate addictions. The size of the CBD stone was correlated with the patient’s age (r=0.17, P=0.001). The average stone measured 12.22±3.32 mm. There were notable differences in the mean size of the CBD stone (P<0.001) between addicted and non-addicted cases; specifically, the mean CBD stone size in addicted cases was 12.715.13 mm while it was 12.34.33 mm in non-addicted cases.Conclusion: This study showed patients with CBD stones have a higher rate of opium addiction compared to the general population, indicating a possible link between the two conditions

Family History of Alzheimer’s Disease Increases the Risk of COVID-19 Positivity: A SUMS Employees Cohort-based Study

Background: Substantial data indicate that genetic and environmental factors play a key role in determining the risk of Alzheimer’s disease (AD). Moreover, it is known that having relatives with AD increases the risk of developing this disease.Objective: This study is aimed at investigating whether having a family history of AD, may increase the risk of COVID-19 in a cohort-based study.Material and Methods: Participants of this retrospective cohort study were previously enrolled in the SUMS Employees Cohort (SUMSEC). All participants including those whose SARS-CoV-2 infection was confirmed by positive PCR test and chest CT scan were requested to respond to interviewer-administered questionnaires. Moreover, AD was diagnosed via memory and thinking impairment, concentration problems, confusion with location, and problems in finishing daily tasks.Results: The total numbers of female and male participants with a family history of AD were 463 and 222 individuals, respectively. When all types of family history of AD were considered, a 51.3% increase was found in the relative frequency of the participants with both family history of AD and confirmed COVID-19 compared with those only with a family history of AD. Conclusion: Despite the limitations of our study, and from a broader perspective, our findings can further support the concept that AD risk haplotypes including APOE are linked to the same morbidities from cardiovascular disease and obesity that increase vulnerability to COVID-19. Given this consideration, millions of APOE ε4 carriers around the globe should be advised to take additional precautions to prevent life-threatening diseases such as COVID-19

Intelligent Multi-Agent Systems for Advanced Geotechnical Monitoring

Geotechnical monitoring, essential for ensuring the safety and longevity of infrastructures, has predominantly relied on centralized systems. However, as computational capabilities soar and advancements in Artificial Intelligence (AI) burgeon, the potential for decentralized solutions comes to the fore. This chapter intricately weaves the principles and applications of Multi-Agent Systems (MAS) into the fabric of geotechnical monitoring. It delves deep, elucidating the decentralized approach to monitoring aspects like soil quality and groundwater levels. Through a seamless interplay between agents, we witness real-time data acquisition, intricate analysis, and informed decision-making. While anchoring itself in theoretical foundations, the chapter also illuminates the real-world challenges and proffers potential solutions in geotechnical engineering, thereby mapping the past, present, and future of MAS in this domain

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Within the domain of geotechnical engineering, risk assessment is pivotal, acting as the linchpin for the safety, durability, and resilience of infrastructure projects. While traditional methodologies are robust, they frequently require extensive manual efforts and can prove laborious. With the onset of the digital era, machine learning (ML) introduces a paradigm shift in geotechnical risk assessment. This chapter delves into the confluence of ML and geotechnical engineering, spotlighting its enhanced predictive capabilities regarding soil behaviors, landslides, and structural resilience. Harnessing modern datasets and rich case studies, we offer an exhaustive examination that highlights the transformative role of ML in reshaping geotechnical risk assessment practices. Throughout our exploration of evolution, challenges, and future horizons, this chapter emphasizes the significance of ML in advancing and transforming geotechnical practices