Search Results (224)

We propose an unsupervised method for eigen tree hierarchies and quantisation group association for segmentation of corrosion in marine vessel hull inspection via camera images. Our unsupervised approach produces image segments that are examined to decide on defect recognition. The method generates a binary decision tree, which, by means of bottom-up pruning, is revised, and dominant leaf nodes predict the areas of interest. Our method is compared with other techniques, and the results indicate that it achieves better performance for true- vs. false-positive area against ideal (ground truth) coverage. Full article

Mastering construction times is of paramount importance in making vernacular earth construction techniques attractive to modern clients. The work presented here is a contribution towards the optimization of the construction time of cob buildings. Therefore, this paper follows the evolution of a cob’s mechanical properties during its drying process in the case of a double-walling CobBauge system. Laboratory tests and in situ measurements were performed, and further results were described. Volumetric water content sensors were immersed in the walls of a CobBauge prototype building during its construction. The evolution of the cob layer’s compressive strength and Clegg Impact Value (CIV) as a function of its water content has been experimentally studied and discussed. These studies showed that compressive strength and CIV are correlated with water content, and both properties decrease exponentially with time. In this study, a new tool to evaluate cob’s mechanical performances in situ has been proposed, Clegg Impact Soil Tester. This was linked to compressive strength, and a linear relationship between these two properties was found. Finally, appropriate values of compressive strength and CIV to satisfy before formwork stripping and re-lifting were proposed. For this study’s conditions, these values are reached after approximately 27 days. Full article

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is destructive requiring manual labor and may cause damages for the plants. In this work, we report on the AI-based approach for assessing and predicting the leaf area and plant biomass. The proposed approach is able to estimate and predict the overall plants biomass at the early stage of growth in a non-destructive way. For this reason we equip an industrial greenhouse for cucumbers growing with the commercial off-the-shelf environmental sensors and video cameras. The data from sensors are used to monitor the environmental conditions in the greenhouse while the top-down images are used for training Fully Convolutional Neural Networks (FCNN). The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy. Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics. Then we established the dependency between the average leaf area and biomass using the direct measurements of the biomass. This in turn allowed for reconstruction and prediction of the dynamics of biomass growth in the greenhouse using the image data with 10% average relative error for the 12 days prediction horizon. The actual deployment showed the high potential of the proposed data-driven approaches for plant growth dynamics assessment and prediction. Moreover, it closes the gap towards constructing fully closed autonomous greenhouses for harvests and plants biological safety. Full article

Green building materials have nontoxic properties and are made from recycled materials. This means they are, in most cases, created from renewable resources in comparison to non-renewable resources. This research aims to further improve the justification of green buildings and their materials. This is undertaken to determine the validity of such construction techniques. This research utilizes both qualitative and quantitative methods through five Australian case studies. The case studies, which are based on new and redeveloped structures, are selected via different geological locations and are evaluated via logical argumentation along with correlation research. Further, the research will address the problem by identifying a variety of green building materials that can be used to substitute non-green building materials. With careful comparisons among the five buildings, the green signs and implementation advantages and disadvantages will be evaluated. The result of this comparison will assist in improving the current education around the topic of green building and benefit the overall response to positive change within the construction industry. Although green building initiatives are not difficult to apply, they can be cost efficient. To maximize their cost efficiency, these initiatives need to be fully adopted. This includes the adaptation of specific building orientation, design, and sealing off penetrations to greatly improve passive heating and cooling. Further, the use of rainwater tanks also assists with energy efficiency by reducing the amount of mains water used. The utilization of natural lighting along with an advanced solar power system would further reduce the overall energy usage. Full article

While damping devices can provide supplemental damping to mitigate building vibration due to wind or earthquake effects, integrating them into the design is more complex. For example, the Canadian code does not provide building designs with inline friction dampers. The objective of this present article was to study the overstrength, ductility, and response modification factors of concrete frame buildings with inline friction dampers in the Canadian context. For that purpose, a set of four-, eight-, and fourteen-story ductile concrete frames with inline seismic friction dampers, designed based on the 2015 National Building Code of Canada (NBCC), was considered. The analyses included pushover analysis in determining seismic characteristics and dynamic response history analysis using twenty-five ground motion records to assess the seismic performance of the buildings equipped with inline seismic friction dampers. The methodology considered diagonal braces, including different 6 m and 8 m span lengths. The discussion covers the prescribed design values for overstrength, ductility, and response modification factors, as well as the performance assessment of the buildings. The results revealed that increasing the height of the structure and reducing the span length increases the response modification factors. Full article

Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration. Full article

The redesign of a failed hoisting shaft belonging to a 10 m stroke vertical transfer device (VTD) is presented. Firstly, the operation of the VTD is thoroughly analysed, the variation of loads and moments along the operating cycle is characterised, and transients such as emergency stop loads are calculated. The selection of safety factors and duty cycle factors was followed by the shaft sizing. After an initial rough sizing, the high-cycle fatigue (HCF) design for cyclic bending moments was performed, first considering constant torque and then considering cyclic torque. The number of bending and torsion cycles performed by the hoisting shaft over 10 years was shown to exceed 10⁶, and an infinite life design is mandatory. The analyses showed that the initial shaft diameter was insufficient, thus justifying the failures observed before the present redesign. A classical fatigue model combining torsional shear stresses with bending stresses was used to take into account reversed torsional loading and ensure infinite fatigue life. This work highlights the need to thoroughly understand a machine’s operating cycle so that the wrong premises for fatigue design calculations are not assumed. Full article

The necessity for environmentally friendly transportation systems has prompted the proliferation of electric vehicles (EVs) in low-voltage (LV) distribution networks. However, large-scale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid. It is therefore important to assess the impact of connecting EVs for charging in existing distribution networks and determine the hosting capacity (HC) of such a network. This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively, for a South African low-voltage distribution network containing 21 households. Voltage drop and equipment loading are the performance indices (PI) considered for the impact assessment and HC estimation in this study. The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits. The results show that the HC is 5–8 three-phase EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario. Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively. The result showing the seasonal variation in HC and for other EV charging power is also presented. The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC. Full article

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance. Full article

Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters including dissolved oxygen, specific conductance, discharge, water level, temperature, pH, and turbidity is crucial in the field of water resource management. This is especially important during a time of rapid climate change, where weather patterns are constantly changing, making it difficult to forecast these SW variables accurately for different water-related problems. Various numerical models based on physics are utilized to forecast the variables associated with surface water (SW). These models rely on numerous hydrologic parameters and require extensive laboratory investigation and calibration to minimize uncertainty. However, with the emergence of data-driven analysis and prediction methods, deep-learning algorithms have demonstrated satisfactory performance in handling sequential data. In this study, a comprehensive Exploratory Data Analysis (EDA) and feature engineering were conducted to prepare the dataset, ensuring optimal performance of the predictive model. A neural network regression model known as Long Short-Term Memory (LSTM) was trained using several years of daily data, enabling the prediction of SW variables up to one week in advance (referred to as lead time) with satisfactory accuracy. The model’s performance was evaluated by comparing the predicted data with observed data, analyzing the error distribution, and utilizing error matrices. Improved performance was achieved by increasing the number of epochs and fine-tuning hyperparameters. By applying proper feature engineering and optimization, this model can be adapted to other locations to facilitate univariate predictive analysis and potentially support the real-time prediction of SW variables. Full article

The accurate determination of key parameters, including the CO₂-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO₂-enhanced oil recovery (CO₂-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The performance evaluation of the models using statistical indicators revealed impressive coefficients of determination of unseen data (0.9807 for dead oil solubility, 0.9835 for live oil solubility, 0.9931 for CO₂-n-Alkane interfacial tension, and 0.9648 for minimum miscibility pressure). One notable advantage of our models is their ability to predict values while accommodating a wide range of inputs swiftly and accurately beyond the limitations of common correlations. The dataset employed in our study encompasses diverse data, spanning from heptane (C₇) to eicosane (C₂₀) in the IFT dataset, and MMP values ranging from 870 psi to 5500 psi, covering the entire application range of CO₂-EOR. This innovative and robust approach presents a powerful tool for predicting crucial parameters in CO₂-EOR projects, delivering superior accuracy, speed, and data diversity compared to those of the existing methods. Full article

With the interest aroused by the development of modern concretes such as printable or self-compacting concretes, a better understanding of the rheological behavior, directly linked to fresh state properties, seems essential. This paper aims to provide a phenomenological description of the rheological behavior of cement paste. The first part is devoted to the most common testing procedures that can be performed to characterize the rheological properties of cement suspensions. The second one deals with the complexities of the rheological behavior of cement paste including the non-linearity of flow behavior, the viscoelasticity and yielding, and the structural build-up over time. Full article

Considering the various problems caused by infiltration in civil construction, this study aimed to identify the most appropriate waterproofing methods for different types of surfaces. A study was conducted on the mechanisms of water infiltration on surfaces and the waterproofing methods available on the market, focusing on asphalt blankets, in addition to a literature review highlighting state-of-the-art methods on this topic. A case study was also conducted in a residence in Nova Lima, Brazil, analyzing different waterproofing techniques, including their characteristics and stages. Among the conclusions, it is highlighted that the implementation of adequate project, installation, inspection, and maintenance techniques can significantly reduce the waterproofing failure rate and repair costs, and that the excellent choice of materials, along with the skill of the labor force in the application, is fundamental to guarantee the adequate performance of these materials in buildings. Full article

Estimation of the potential consequences from events occurring downstream of a dam is part of the risk assessment needed during the installation phase of a new dam. In the case of specific natural or man-made ongoing or prospected events, it may also be important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool. The analysis shows that in a few minutes, it is possible to obtain the input deck for a new case. This makes it possible to include the prospected methods into automatic routines in analytical tools such as the Global Disasters Alerts and Coordination System (GDACS) to have a quick overview of the expected flood due to a dam break event. Full article

As a country, Pakistan is mostly dependent on fossil fuels for fulfilling its energy demand, which is expensive, as well as being environmentally unfriendly. It is high time that the country decides to shift from fossil fuels to renewable energy resources like geothermal, wind, solar, etc., to cater for global warming issues. Pakistan has a lot of potential geothermal sites, as the location of Pakistan lies on several fault lines and hot springs, thus making it very easy to extract the temperature from deep inside the earth and harness it for Geothermal Energy. Also, a sound knowledge of ground temperature is essential to use geothermal energy, which is obtained by drilling boreholes and putting in sensors. However it becomes a very expensive and labor intensive procedure. Therefore, to avoid the huge cost for drilling boreholes, particularly for ground temperature analysis, a numerical approach has been considered for determining ground temperature. Furthermore, correlation charts between air and ground temperatures have been developed, as there were no proper studies on the ground temperature of Pakistan. Then, with the help of a boreholes drilled in the National University of Sciences and Technology, Islamabad, Pakistan, the actual ground and numerically calculated temperatures have been compared. The results show a temperature error margin in the range between 0.27% for higher depths of about 5.6 m and 7.3% near the surface of about 2.7 m. Thus, it is shown that the proposed method is easy to implement and better than large scale testing methods for the depths at which geothermal energy is extracted. Full article