Eng

The increasing demand for deep excavations in construction projects emphasizes the necessity of robust support structures to ensure safety and stability. Support structures are critical in stabilizing excavation pits, with a primary focus on enhancing their bearing capacity. This paper employs finite element modeling techniques to conduct a numerical analysis of nails and helical anchors’ bearing capacity. To reinforce the stability of pit walls, selecting an appropriate method for guard structure construction is imperative. The chosen method should efficiently redistribute forces induced by soil mass weight, displacements, and potential loads in the pit vicinity to the ground. Various techniques, including trusses, piles, cross-bracing systems, nailing, and anchorage systems, are utilized for this purpose. The study evaluates numerical models for two guard structure configurations: nailing systems and helical anchorage. It examines the impact of parameters such as displacement, helical helix count, helix diameter variations, and the integration of nailing systems with helices. Comparative analyses are conducted, including displacement comparisons between different nailing systems and helical anchor systems, along with laboratory-sampled data. The research yields significant insights, with a notable finding highlighting the superior performance of helical bracings compared to nailing systems. The conclusions drawn from this study provide specific outcomes that contribute valuable knowledge to the field of deep excavation support structures, guiding future design and implementation practices. Full article

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process. Full article

We address the challenges of limescale deposition and its management in urban construction sites, specifically within the Sumayil North project in Tel Aviv. Jet grouting, a method increasingly favored over conventional dewatering techniques for its minimal environmental impact and efficiency, is scrutinized for its unintended consequences on groundwater chemistry, particularly in relation to limescale formation. Our investigation centers on a dual approach: dissecting the geochemical dynamics leading to limescale deposition following jet grouting operations, and evaluating a remedial acid injection strategy implemented to counteract this phenomenon. We identify the critical factors influencing aquifer water chemistry through a detailed hydro-chemical analysis encompassing the Pleistocene Coastal Aquifer’s dynamics. The study reveals that the interaction between grout components and aquifer water significantly alters groundwater pH, driving the precipitation of calcium carbonate. The subsequent implementation of a sulfuric acid injection regimen successfully mitigated limescale accumulation, restoring pumping efficiency and neutralizing pH levels. We propose a workflow to manage and prevent limescale, emphasizing preemptive measures like custom grout compositions and controlled dewatering, with strict post-intervention groundwater monitoring. This approach balances operational efficiency, infrastructure integrity, and environmental stewardship in urban construction projects interfacing with sensitive aquifer systems. Full article

Microorganisms (MOs) are prominent in ecological functioning and balance. The rhizosphere is considered one of the most diverse ecosystems on Earth and serves as a breeding spot for many MOs. Rhizosphere microbial diversity changes according to plant species, genotype, and the nature of the soil. The current study reports the possible use of bacteria isolated from the rhizosphere of Azadirachta indica for synthesizing silver nanoparticles (AgNPs). The physicochemical characterization and antibacterial activity of these green synthesized AgNPs are also reported. The gene (16S rRNA) sequence of bacteria isolated from the rhizosphere showed a maximum similarity of 99.25% with Bacillus subtilis. After incubation, the colorless reaction mixture transformed to brown, which indicates the formation of AgNPs, and UV-vis spectral analysis also confirmed the biosynthesis of AgNPs. Compared to lower temperatures, the efficiency of AgNP synthesis was high at the higher temperature. The scanning electron microscope image demonstrated spherical-shaped AgNPs with sizes ranging from 18 to 21 nm. Energy-dispersive X-ray analysis established the elemental analysis of synthesized AgNPs. The synthesized AgNPs showed strong bactericidal properties against pathogenic bacteria Klebsiella pneumonia, Pseudomonas aeruginosa, Escherichia coli, and methicillin-resistant Staphylococcus aureus. Full article

The highly crystalline ZnAl layered double hydroxides (ZnAl-NO₃-LDHs) are utilized for the potential transformation into mixed metal oxides (MMOs) through thermal decomposition and used further for the photodegradation of phenol to assess the influence of calcination on ZnAl-LDHs with enhanced photoactivity. The structure, composition, and morphological evolution of ZnAl-LDHs to ZnO-based MMO nanocomposites, which are composed of ZnO and ZnAl₂O₄, after calcination at different temperatures (400–600 °C), are all thoroughly examined in this work. The final ZnO and ZnAl₂O₄-based nanocomposites showed enhanced photocatalytic activity. The findings demonstrated that calcining ZnAl-LDHs from 400 to 600 °C increased the specific surface area and also enhanced the interlayer spacing of d₀₀₃ while the transformation of LDHs into ZnO/ZnAl₂O₄ nanocomposites through calcining the ZnAl-LDH precursor at 600 °C showed significant photocatalytic properties, leading to complete mineralization of phenol under UV irradiation. Full article

The oil and gas industry faces significant challenges due to wear on drilling motor components, such as thrust pins and inserts. These components are critical to the efficiency and reliability of drilling operations, yet are susceptible to wear, leading to significant economic losses, operational downtime, and safety risks. Despite previous research on wear-resistant materials and surface treatments, gaps exist in understanding the unique properties of thrust pins and inserts. The aim of this study is to enhance mechanical system performance by characterizing the wear resistance of these components. Through chemical analysis, hardness assessments, and metallographic examinations, the study seeks to identify specific alloys and microstructures conducive to wear resistance. Key findings reveal that AISI 9314 thrust pins exhibit superior wear resistance with a tempered martensite microstructure and a hardness of 41 HRc, whereas AISI 9310 inserts are less resistant, with a hardness of 35 HRc. The research employs advanced techniques, including a pin-on-disc tribometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and profilometry, to evaluate wear behavior, visualize wear patterns, analyze elemental composition, and quantify material loss and surface roughness. Our findings demonstrate that optimizing the material selection can significantly enhance the durability and efficiency of drilling motors. This has profound implications for the oil and gas industry, offering pathways to reduce maintenance costs, improve operational efficiency, and contribute to environmental sustainability by optimizing energy consumption and minimizing the carbon footprint of drilling operations. Full article

The problem addressed in this article consists of the motion control of a quadrotor affected by model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning, is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow for an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor built from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on. Full article

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased robotization and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision assembly such as station layout, tooling design and robot programming are discussed. The findings highlight the benefits of robots for nonstop operation, freeing up human operators for higher value tasks despite longer cycle times. In addition, the paper advocates further research into reliable gripping of small components, a current challenge for robotics. The work contributes to open science by sharing partial results and methods that could inform future problem solving in robotic assembly. Full article

Ubiquitous street lighting is essential for urban areas. While nowadays, LED-based “smart lamps” are commercially available, municipalities can only switch to them in the long run due to financial constraints. Especially, older types of lamps require frequent bulb replacements to maintain the lighting infrastructure’s function. To speed up the detection of defects and enable better planning, a non-invasively retrofittable IoT sensor solution is proposed that monitors lamps for defects via visible light sensors, communicates measurement data wirelessly to a central location via LoRaWAN, and processes and visualizes the resulting information centrally. The sensor nodes are capable of automatically adjusting to shifting day- and nighttimes thanks to a second sensor monitoring ambient light. The work specifically addresses aspects of energy efficiency essential to the battery-powered operation of the sensor nodes. Besides design considerations and implementation details, the paper also summarizes the experimental validation of the system by way of an extensive field trial and expounds upon further experiences from it. Full article

In order to successfully achieve mass production in NAND flash memory, a novel test procedure has been proposed to electrically detect and screen the channel hole defects, such as Not-Open, Bowing, and Bending, which are unique in high-density 3D NAND flash memory. Since channel hole defects lead to catastrophic failure (i.e., malfunction of basic NAND operations), detecting and screening defects in advance is one of the key challenges of guaranteeing the quality of flash products in the NAND manufacturing process. Based on analysis of the physical and electrical mechanisms of the channel hole defect, we have developed a two-step test procedure that consists of pattern-based and stress-based screen methodologies. By optimizing test patterns depending on the type of defect, the pattern-based screen is effective for detecting the type of Hard channel hole defects. The stress-based screen is carefully implemented to detect hidden Soft channel hole defects without degrading the reliability of NAND flash memory. In addition, we have attempted to further optimize the current version of our technique to minimize test time overhead, thus enabling 72.2% improvement in total test time. Experimental results using real 160 3D NAND flash chips show that our technique can efficiently detect and screen out various types of channel hole defects with minimum test time and negligible degradation in the flash reliability. Full article

The revised Energy Performance of Buildings Directive (EPBD) recognizes nearly zero-energy buildings (nZEB) and building renovation as essential steps in the decarbonization of the built environment. A thorough understanding of existing buildings is a prerequisite for improving their thermal performance and ensuring that interventions are based on pre-existing conditions. This study investigates the use of thermography as a tool for identifying construction solutions and assessing the thermal performance of buildings. Initially, it addresses the benefits and limitations of this technique, as well as some results of a qualitative analysis and standard application of this technology. Specific conditions for capturing images on-site were identified, along with the relevant factors for interpreting thermograms under natural conditions. These images enabled the identification of previous works, changes in buildings, and the use of different materials and construction techniques, thereby contributing to the characterization of buildings. Consequently, they can be used in the diagnostic phase to enhance the accuracy of intervention solutions based on a better understanding of existing conditions. Full article

Samples of Sr₄Al₆O₁₂SO₄ are obtained through a solid-state reaction of Al₂O₃, SrSO₄, and SrCO₃. The samples are then made into 1 and 4 cm pellets by compacting them at 100MPa and sintering them at 1400 °C for 4 h. The compound is analyzed using X-ray diffraction. Static immersion and wettability tests are carried out to evaluate corrosion resistance in contact with Al–Si. Corrosion tests are conducted by immersing the samples at 800, 900, and 1000 °C for 24, 50, and 100 h, while wettability is studied at 900, 1000, and 1100 °C for 2 h. Afterwards, the samples are subject to metallographic preparation. The samples are then analyzed using optical microscopy, scanning electron microscopy, and image analysis. In general, reaction products consisting of alumina, spinel, oxides, and sulfates are found. The contact angles obtained are between 124° and 135°. It is concluded that the Sr₄Al₆O₁₂SO₄ ceramic substrate is resistant to corrosion by the Al–Si alloy because of the slight thickness of the reaction products found in the samples (73 μm), considering the severe conditions of the experiment: 1000 °C and 100 h of isothermal temperature. Furthermore, Sr₄Al₆O₁₂SO₄ is not wettable by Al–Si alloys. These results suggest that the ceramic substrate could be used in the refractory industry, possibly as an additive to commercial refractory ceramics. For future work, it is recommended to carry out the same study with the aluminum–magnesium alloy and as an additive in commercial refractory ceramics. Full article

This paper considers the application of a scenario simulation technique to assess subsurface drip irrigation system efficiency while using it to irrigate crops grown under raised bed technology. For simulating purposes, we use a model based on the two-dimensional Richards equation stated in terms of water head in a curvilinear domain. Solutions to problems are obtained using a finite-difference scheme with dynamic time step change. Using the data from pressure measurements obtained while growing potatoes on sandy loess soil in production conditions, we performed a calibration of the model using the particle swarm optimization algorithm. Further, the accuracy of the model was tested and average absolute errors in the range from 3.16 to 5.29 kPa were obtained. Having a calibrated model, we performed a series of simulations with different irrigation pipeline placements determining the configuration under which water losses are minimal. The simulated configuration, under which infiltration losses were minimal, was the installation of pipelines under the raised bed at the depth of 10 cm below the soil surface. The results confirm that the applied technique can be used for decision-making support while designing subsurface drip irrigation systems combined with raised bed growing technology. Full article

Random Numbers are widely employed in cryptography and security applications. This paper presents a novel approach to generate high-quality random bitstreams by harnessing the inherent noise properties of ring oscillators. We implemented ring oscillators with varying numbers of stages (3, 5, and 7), different geometries and different startup voltages in Cadence and recorded their total output power, which includes the cumulative noise effects. Subsequently, we exported these power measurements to MATLAB, where we applied a Fast Fourier Transform (FFT)-based technique to extract the total noise characteristics for each ring oscillator. Using the obtained noise data, we generated separate random bitstreams of 10 million bits for the 3-stage, 5-stage, and 7-stage ring oscillators. The final random bitstream, consisting of 10 million bits, was created by performing a bitwise XOR operation on the bitstreams generated by each ring oscillator. The degree of randomness of the generated bitstreams was assessed using the NIST 800-22 statistical test suite. Remarkably, the final random bitstream exhibited strong robustness and suitability for cryptographic applications. This innovative approach leverages the noise properties of ring oscillators to create reliable random bitstreams, offering potential applications in secure communications and cryptography. The results highlight the feasibility of using ring oscillators as noise sources for random bit generation and underscore their effectiveness in meeting stringent randomness criteria. Full article

The main aim of this study was to identify and evaluate the characteristic properties of bitumen modified with algae. Two types of algae, each with distinct gradation and origin, were employed for this investigation. For each type of algae (noted as chlorella and microchlorella), three blends were created with varying algae contents (5%, 10%, and 15% by weight of bitumen), utilizing a 70/100 reference bitumen as the virgin material and a basis for comparison. The properties of the blends were investigated using the Penetration, Softening Point, Elastic Recovery, Force Ductility, Dynamic Viscosity, and Storage Stability tests, both before and after short-term ageing (TFOT). The test results were then used to calculate the Activation Energy (E_a), Viscosity-Temperature Susceptibility (VTS) Index, and Mixing Temperature (T_mixing), along with their respective Pearson Correlation Coefficient (PCC) and R² and p-values. The main finding of the study was that the addition of a low algae content of 5% caused a change in the classification of the unaged bitumen from 70/100 to 50/70 according to EN 12591 and thus hardened the reference bitumen. Additionally, a strong linear statistical correlation was observed between E_a and the VTS index, suggesting that these values should be considered when characterizing the temperature susceptibility of algae-modified bitumen. Full article

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners. Full article

Structures are recommended to be designed and constructed with the integration of structural health monitoring techniques to ensure that they can dissipate a large amount of energy without considerable damage when subjected to earthquakes. Hysteretic (H), friction (F), viscous (V), and viscoelastic (VE) dampers were employed in this study to observe the response of buildings using the commercially available software ETABS. The effect of different dampers along with configurations on three prototype concrete buildings (3, 5, and 10-storey) was studied by performing a time history analysis. Initially, the response of the buildings was observed in terms of storey drifts, base shear, and displacement without using dampers, while gradually increasing the damping ratio from 0 to 40%. Subsequently, the response of the buildings was evaluated in terms of displacements and base shear using various types of dampers with different configurations. The analysis results demonstrated that the effectiveness of viscous and viscoelastic dampers is higher for 3 and 5-storey buildings, while friction and hysteresis dampers are more suitable for 10-storey buildings. This information enables informed decisions regarding the performance and maintenance of dampers, contributing to the overall resilience and durability of structures in seismic events. Full article

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury. Full article