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2007 research outputs found
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Performance of Soil Biogrouting as a Subgrade Material of the Road Pavement
No full textSoft clay subgrade is unsuitable for road pavement because it has low bearing capacity and CBR value. Therefore, the soil needs stabilization, but with a sustainable stabilization method. One of these methods is biogrouting, namely grouting, which uses bacteria. Thus, the main objective of this study was to determine the performance of Bacillus subtilis and Bacillus amyloliquefaciens bacteria in stabilizing the soil. The performance of these bacteria was quantified by the CBR value and soil-bearing capacity experimentally in a laboratory model test with each soil thickness of 0-30 cm. The CBR value of the soil improved by the biogrouting method by about 4 times the CBR value of untreated soil. The increase in bearing capacity was obtained about 4 times for treated soil with Bacillus subtilis and about 5 times for treated soil with Bacillus amyloliquefaciens. The layer thickness significantly improves the performance of the subgrade at a layer thickness of 20 cm. The new result of this study is that both bacteria are native Indonesian bacteria, so they are suitable for use in Indonesia. In addition, Bacillus amyloliquefaciens has never been used in research to increase soil-bearing capacity
Predicting the UCS of Industrial Byproduct-Based CLSM Using Machine Learning and Experiments
No full textThis study investigated the development of sustainable Controlled Low Strength Material (CLSM) using industrial by-products pond ash, fly ash, and red mud as alternatives to conventional concrete constituents. This research employs a dual methodology: comprehensive experimental testing aligned with ASTM standards and the implementation of advanced machine learning (ML) techniques to predict the unconfined compressive strength (UCS) of CLSM mixes. Experimental datasets, generated through the variation of key material and mix design parameters, were utilized to train ensemble-based supervised ML models, including ADAboost, XGBoost, gradient boosting machine (GBM), and random forest (RF). A comparative performance evaluation was conducted, and the XGBoost model emerged as the most accurate predictor, achieving R² values of 0.969 for training and 0.933 for testing, surpassing GBM, ADAboost, and RF across multiple performance indicators. The optimal model was subsequently embedded into a graphical user interface (GUI) for UCS prediction. A sensitivity analysis based on the XGBoost model revealed that cement, water, and curing age were the most influential parameters affecting UCS, with cement exhibiting the highest impact value of 0.86 and a relative contribution of 19%. These findings emphasize the significance of these variables in strength development and mix optimization. The integration of experimental validation with predictive modeling not only advances the understanding of CLSM behavior but also underscores the utility of ML in the formulation of sustainable construction materials. This research supports the beneficial reuse of industrial waste, aligns with environmental sustainability goals, and provides an efficient and reliable tool for CLSM mix design
A Procedure for Nonlinear Analysis of Laterally Loaded Single Piles and Pile Groups
No full textThis research introduces an analytical procedure for simulating the nonlinear behavior of single piles and pile groups under lateral loads in multi-layered, heterogeneous soil. The methodology combines the finite element method, the p-y technique, and the p-multiplier concept. Duncan and Chang's hyperbolic equation, characterized by three parameters, was employed to represent the soil reaction for sand and clay soils. A newly proposed equation to derive p-multipliers as a function of a pile's location and spacing within a pile group. Its predictions show satisfactory agreement with those from existing methods. The procedure was implemented in a computer program to enable rapid and accurate computation. The proposed program validation involved comprehensive comparisons against results from field load tests and sophisticated 3D finite element analyses. These comparisons confirm that the developed program is both reliable and efficient, making it well-suited for preliminary design stages. A subsequent parametric study on a single pile revealed that replacing soft upper clay with a compacted sand layer significantly decreases lateral deflection and bending moment. For the cases examined, an optimal compacted layer thickness of three pile diameters and a stiffness 5.6 times that of the native soft clay were identified
Evaluation of Flood Inundation Image Detection Performance Using Deep Learning
No full textFloods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems
Performance of Sustainable Underwater Concrete Containing GGBS and Micro Silica with Anti-Washout
No full textAnti-washout concrete (AWC) is engineered for underwater constructions, with resistance to dispersion achieved through the use of anti-washout admixtures (AWAs). This study experimentally investigated the design of sustainable anti-washout concrete mixtures containing a high content of by-product waste materials. The study aims to evaluate sustainable underwater concrete mixtures with high supplementary cementitious materials content, analyze the influence of AWA on compressive strength, and assess the compatibility of anti-washout admixture with both SCMs and superplasticizers. However, the interaction of AWA with a high content of ground granulated blast furnace slag (GGBS) and microsilica in underwater concrete has not been previously investigated. Two groups of concrete mixtures were developed: the first group consisted of two sustainable mixtures, with and without AWA, containing 52.15% ordinary Portland cement (OPC), 43.5% GGBS, and 4.35% micro silica. The second group consisted of two conventional mixtures: one with 100% OPC and the other with 100% OPC plus AWA. Fresh properties, such as slump flow, viscosity (measured by the V-funnel), and air content, were evaluated. Compressive strength was measured to assess mechanical performance. Durability was investigated using four tests: rapid chloride penetration tests (RCPT), water penetration, water absorption, and initial surface absorption tests (ISAT). An anti-washout test was conducted to determine the effectiveness of AWC in minimizing the washout of cement particles. The mixture design introduces an innovative approach to utilizing high levels of SCMs for producing high-strength, durable, and sustainable AWC. The durability results showed that the ISAT test was ineffective for evaluating concrete performance underwater. This research contributes to understanding the effects of AWAs and their compatibility with superplasticizers and SCMs. AWA forms a thixotropic gel that protects cement particles from washout and is highly compatible with superplasticizers
Measurement Invariance of Expectations Toward Sustainable Public Transport Service Quality Among Urban and Rural Older Adults
No full textThis study examines measurement invariance of expectations toward sustainable public transport service quality between urban and rural older adults in Thailand. Using second-order confirmatory factor analysis, data were collected from 1,189 elderly respondents across Thailand's four major regions through face-to-face interviews. The measurement framework incorporated eleven service quality dimensions: nine traditional attributes (Vehicle, Bus Stop, Accessibility, Convenience, Information, Staff, Safety and Security, Reliability, and Affordability) and two extended dimensions (Older's Facilities and Post-Pandemic Prevention). Results demonstrated successful measurement invariance, confirming that the eleven-factor structure operates equivalently across urban and rural contexts. Universal priorities emerged for Convenience, Staff quality, and Reliability, while rural elderly showed elevated importance for Safety and Security. The validation of Older's Facilities and Post-Pandemic Prevention as distinct dimensions establishes empirical support for incorporating age-inclusive design and health protection measures as permanent components of sustainable transport planning, justifying unified national standards while accommodating regional variations for Thailand's aging population
The Influence of Nanodiamonds and Aluminum Oxide Nanoparticles on the Structure and Properties of High-Strength Concrete
No full textHigh-performance concrete (HPC) is an important construction material that can be improved with nano-additives. In this study, the modification of HPC with KHA-HC nanodiamond and nano aluminum oxide (NA) admixtures was investigated; the admixture rate was applied in the range of 0-1.4% in 0.2% increments. The rheology, density, compressive and flexural strength, water absorption, and microstructure properties were investigated; the results showed that the KHA-HC nanodiamond showed higher efficiency than NA. Compared with HPC without nano-additives, the strength properties of HPC with the most optimal content of nano-additives, 0.6% KHA-HC and 1.0% NA, were improved by 47.1% and 17.0% for compressive strength and by 44.9% and 16.3% for flexural strength. Water absorption decreased by 33.0% and 26.0%, respectively. Also, with optimal dosages of nano-additives, an improvement in the rheology of the HPC mixture was recorded. The complex modification of HPC 0.6% KHA-HC and 1.0% NA provides a synergistic effect and maximum improvements in properties: the increase in compressive strength was 58.2%; flexural strength - 54.1%; decrease in water absorption - 49.1%. HPC modified by nano-additives has an improved macro- and microstructure. The two types of nano-additives’ effectiveness, KHA-HC and NA, both separately and together in HPC technology for additional improvement of their operational properties, has been proven
Improving Thermal Comfort and Air Quality: PET and CO₂ Evaluation of School Courtyard’s Orientation
No full textThis study aims to investigate the thermal conditions related to the variations in the school courtyard’s orientation, focusing on mass temperature (Tm), outdoor air temperature, and Physiological Equivalent Temperature (PET). A qualitative methodology based on ENVI-met software was adopted. Simulations for the existing school building were performed in the four basic orientations on 21 March and 21 September to assess the thermal impact of the courtyard’s orientations. Results showed that orientation produced slight but meaningful differences in Tm, with variations of 0.16°C in September and 0.20°C in March. Though modest, these differences become significant when scaled to the large mass of the school buildings, where even small reductions affect energy demand and comfort. For outdoor air temperature, the south orientation achieved reductions of 0.53–1.13°C in September and 1.1–1.9°C in March compared to ambient conditions. PET and wind maps supported these findings, with the south orientation allowing better airflow and better thermal comfort. Furthermore, analysis of CO₂ concentration confirmed that the south-facing courtyard provided the healthiest air quality. The study highlights that courtyard orientation should not be overlooked in large educational buildings, as even slight orientation-driven improvements become critical, reinforcing the importance of integrating orientation into holistic passive design strategies
Impact of the Application of Smart Sensor Networks for the Construction Management of Geotechnical Activities
No full textThe primary objective of this study is to evaluate the impact of smart sensor networks on geotechnical data management, specifically enhancing accuracy, real-time monitoring, safety, and reliability. To achieve this, data was collected through a survey of 380 geotechnical professionals in Saudi Arabia, with 106 valid responses analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Principal Component Analysis (PCA) and Factor Analysis (FA) were employed to identify the key variables and underlying relationships among them. The findings demonstrate that smart sensor networks significantly improve the accuracy of geotechnical data (path coefficient = 0.662), real-time monitoring and early warning systems (path coefficient = 0.701), safety and risk management (path coefficient = 0.761), and data reliability (path coefficient = 0.410). This study introduces a novel framework integrating advanced statistical methods with smart sensor networks, offering a practical approach to optimizing geotechnical operations. The research highlights the importance of advanced data analytics in enhancing the full potential of smart sensors, presenting an innovative solution for improving decision-making and risk management in geotechnical engineering. These findings provide a significant contribution to sustainable and effective geotechnical practices. Doi: 10.28991/CEJ-2025-011-01-020 Full Text: PD
Experimental Study of the Dynamic Behavior of Stabilised Marl with Lime
No full textThe variable characteristics of weathered marl cause engineering problems, particularly in geotechnics, and require in-depth studies to design structures. Where these characteristics are poor, lime is used to stabilize the soil. Experimental research is being carried out on various Tizi-Ouzou marls composed of different percentages of CaCO3. The aim is to study their behavior in the presence of quicklime and its impact on the evolution of their geotechnical characteristics to provide effective and economical solutions for stabilization. These marls are mixed with increasing percentages of lime and subjected to a series of tests in which cyclic shear is essential for simulating dynamic effects. The results obtained confirm the improvement in their geotechnical characteristics. On the one hand, interstitial pressures and cyclic deformations have decreased, thus avoiding the risk of liquefaction, subsidence, or settlement. On the other hand, cyclic stresses and resistances have increased, resulting in better resistance of these stabilized marls to dynamic stresses. Finally, the number of cycles required to reach failure has increased, thus reducing the risk of pavement damage. These results depend primarily on the percentage of CaCO3in the marl. Doi: 10.28991/CEJ-2025-011-01-09 Full Text: PD