2031 research outputs found
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Circularized and Corner-Rounded Rectangular Reinforced Concrete Columns Wrapped with CFRP Under Eccentric Compression
Get PDFThis paper presents experimental and analytical investigations on the behavior and mechanical properties of carbon fiber reinforced polymer (CFRP) confined circularized and corner-rounded rectangular reinforced concrete (RC) columns under eccentric loading. Twelve RC columns with cross sections of 150×200 mm were tested. Two columns were used as control specimens. Five columns were circularized and then wrapped with five CFRP configurations. The other five columns were corner-rounded and then wrapped with the above configurations. These twelve columns were eccentrically loaded until they failed. The results indicated that CFRP-confined circularized RC columns failed by CFRP rupture at the eccentric side, while CFRP-confined corner-rounded RC columns failed by CFRP rupture localized at corners. The outstanding effectiveness of the circularization method was its increase in the ultimate load of CFRP-confined circularized RC columns by 3.0–4.3 times that of the control columns. In contrast, the corner-rounding method moderately increased the ultimate load of CFRP-confined corner-rounded RC columns by 1.3–1.7 times that of the control columns. The circularization method outstandingly improved the elastic stiffness by 273.9%–419.3% compared with that of control columns, whereas the corner-rounding method exhibited no effect on the elastic stiffness. The rotation ductility of CFRP-confined circularized and corner-rounded RC columns significantly improved to high ductility when confined with more than 1.33 CFRP layers. Theoretical analyses were performed, and simple models were proposed for reasonably estimating the ultimate loads of the CFRP-confined circularized and corner-rounded RC columns under eccentric loading
Sustainable Utilization of Recycled Concrete Powder as Sand Replacement in Cement Mortar Production: Impact of Sand-Cement Ratio
No full textThe construction industry is becoming more interested in recycled concrete sand obtained from concrete waste due to the urgent need for environmentally friendly building materials. This research investigates the mechanical along durability properties of cement mortars made of recycled concrete sand as a full replacement of natural sand. With a fixed water-to-cement ratio of 0.48, five values of sand-to-cement ratio, including 0.50, 1.00, 1.50, 2.00, and 2.75, were used to prepare different mortar mixes to investigate its effect on the behavior of the mortar. Results indicate a decline in workability with an increasing sand-to-cement ratio, with flow values ranging from 137% at a sand-to-cement ratio of 0.5 to 58% at a sand-to-cement ratio of 2.75. The highest compressive strength of 40.3 MPa was observed in the mix with a sand-to-cement ratio of 0.5 at 28 days, while the mix with a sand-to-cement ratio of 2.75 exhibited the lowest strength at 29.8 MPa, attributed to higher internal porosity. The mix of sand to cement of 1.5 demonstrated a balanced performance, achieving a compressive strength of 29.8 MPa and a flow value of 110 ± 5%, making it suitable for practical applications. Water absorption increased with higher sand-to-cement ratios, consistent with increased void content. Microstructural analysis revealed the presence of residual cementitious phases such as belite and calcium hydroxide in recycled concrete sand, contributing to secondary hydration and influencing durability characteristics. Although mortars containing natural sand outperformed recycled concrete sand-based mixtures in strength and workability, recycled concrete sand mortars met the required performance criteria for building, plastering, and non-structural applications. This study supports the viability of recycled concrete sand as a sustainable alternative to natural sand, contributing to resource conservation and waste reduction in the construction industry
Statistical Optimization of Blending Conditions and Performance Evaluation of Optimal Bio-Asphalt Content
Get PDFTo mitigate environmental impacts and promote sustainability in highway construction, this study investigates the optimization of blending conditions and the performance evaluation of bio-modified asphalt binder incorporating bio-asphalt derived from the pyrolysis of waste cooking oil (WCO) and low-density polyethylene (LDPE). A response surface approach was employed to optimize key blending parameters—temperature, speed, and time—based on critical physical properties of the binder. Furthermore, the optimized bio-asphalt binder was further evaluated through rheological performance tests (multiple stress creep recovery and linear amplitude sweep) and mechanical performance tests (Marshall stability, tensile strength ratio, resilient modulus, indirect tensile fatigue, and dynamic creep). The optimal conditions were identified as 130°C, 1000 rpm, and 42.37 min. Statistical validation using ANOVA, residual analysis, leverage, and Cook’s distance confirmed the model’s reliability, with prediction errors remaining below 5%. The bio-modified asphalt binder exhibited enhanced elastic recovery and reduced non-recoverable creep compliance (Jnr), indicating superior resistance to permanent deformation in comparison with the control asphalt binder. Additionally, the bio-modified asphalt mixture demonstrates superior Marshall stability, resilient modulus, tensile strength ratio, retained stability, and resistance to deformation in comparison with the control asphalt binder. These results demonstrate the potential of bio-asphalt as a viable, eco-friendly modifier for asphalt binders in tropical climates
Benchmarking Classical and Deep Machine Learning Models for Predicting Hot Mix Asphalt Dynamic Modulus
Get PDFThe dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures' aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PD
High Initial Concrete Compressive Strength with Variations of Superplasticizer and Silica Fume Additions
Get PDFConcrete is one of the construction materials resulting from a combination of cement, fine aggregate, coarse aggregate, and water, which are mixed into a solid mass and then can be added with minerals (additives) or chemical additives (admixtures). The purpose of this study is to produce high-quality concrete that is optimum at the early age of the concrete so that the concreting time can be shortened, including by adding superplasticizer as a filler and silica fume as an accelerator. This research method involves making a cylindrical test object with a diameter of 15 cm and a height of 30 cm. Then, the concrete mixture is added with silica fume brand Sika Fume and superplasticizer brand Sika Concrete produced by PT. Sika with 19 variations of the mixture composition; the compressive strength test of the concrete is carried out at 3 days, 7 days, and 28 days. The findings are that 75% of concrete samples using additional materials in the form of silica fume and superplasticizer are quite significant in increasing the initial compressive strength of the concrete by up to 30% both at the age of 3 days, as well as at the age of 7 days and 28 days. The use of additional materials in the form of silica fume and superplasticizer in concrete mixtures with the right levels can generally improve the quality of concrete in its initial compressive strength at the age of 3 days or its workability or fluidity. However, silica fume and superplasticizer materials, when entered into concrete, have mutually influenced performance. The innovation is that high-quality concrete that is optimum at an early age of concrete can be done easily and cheaply using materials that are easily found in the field by combining superplasticizer as a filler and silica fume as an accelerator. Doi: 10.28991/CEJ-2025-011-01-07 Full Text: PD
Driver Drowsiness and Alcohol Detection for Automotive Safety Systems
Get PDFDriver drowsiness and alcohol impairment are major causes of traffic accidents, making road safety a main concern. This study highlights the importance of addressing these issues through improved driver monitoring technologies. A prototype combining MQ-3 alcohol sensors, and facial detection was created, integrating with IoT via a Raspberry Pi to monitor and alert on drowsiness and alcohol levels. The developments use the NTHU-DDD dataset, which supports a supervised learning approach to develop a reliable drowsiness detection model. The study explored various machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Gradient Boosting Classifier, and Gaussian Naive Bayes, with Random Forest and Gradient Boosting emerging as top performers, particularly suited to complex non-linear data. The system effectively used supervised learning techniques to differentiate drowsy and non-drowsy images and exhibited consistent accuracy in detecting drowsiness, especially when the driver’s face was centered. However, accuracy decreased when faces were tilted, highlighting areas for refinement. Moreover, the environmental tests on the MQ-3 sensor demonstrated its sensitivity to alcohol presence, even distinguishing the intensity based on beverage type and concentration. The findings underscore the efficacy of using sensor-based technologies in real-world conditions and provide a foundation for optimizing the system's detection capabilities across various scenarios
Comprehensive Assessment for Liquefaction Vulnerability in Indonesia: Empirical and Element Simulation Approaches
Get PDFHistorical liquefaction events have occurred at many locations, such as Yogyakarta and Lombok; the most significant flow side is in Palu. The standard Indonesian liquefaction assessment is based on a simplified empirical analysis. However, these methods only occasionally yield appropriate results. Contrastingly, the limited data from the cyclic test ensured that the liquefaction ratio could only partially support the liquefaction vulnerability. This research aims to re-examine the empirical approach that combines the constitutive model using LIQCA with a cyclic triaxial test (CXT) and cyclic simple shear (CSS). The empirical method was arranged using deterministic and probabilistic approaches, and the recommendation of the peak ground acceleration (PGA) threshold was validated. The results show a strong relationship between all calculation methods and the SPT value, which differs in the liquefaction strength ratio. This output offers the PGA recommendation results, reaching a 48% overestimation from the empirical method without considering the cyclic test. This research presents the development of a combination of the empirical method with the element simulation from CXT and CSS. This offers a comprehensive overview of the Indonesian requirement standard assessment for liquefaction vulnerability analysis. Doi: 10.28991/CEJ-2025-011-01-019 Full Text: PD
Sedimentation Characteristics and Sediment Transport in the Palu River Estuary
Get PDFSedimentation is the process through which materials transported by water flow settle within that water. Changes in current patterns, driven by tides and variations in current velocity, can influence sediment transport. This study aimed to identify sediment transport patterns and analyze the characteristics of suspended sediment and bed load in the Palu River estuary. We employed field investigations, two-dimensional (2D) numerical modeling, and data analysis to process the findings. The results indicated that the sediment characteristics in the Palu River estuary varied, with a predominance of sand and gravelly sand. Additionally, sediment transportation patterns were found to be primarily influenced by river flow discharge rather than tidal effects. The research findings are presented in a correlation equation that illustrates the relationship between dimensionless parameters: C = Ïs.(a.ψ)bwith coefficient values of a = 412.71 and b = (-0.545). The results of this correlation equation indicate that as the energy from water movement increases, sediment becomes more dispersed, leading to changes in the concentration of sediment particles. It can be concluded that various variables affect sediment transport due to hydrodynamic conditions. Doi: 10.28991/CEJ-2025-011-02-03 Full Text: PD
Landslide Susceptibility Assessment Using Combined TRIGRS and Flow-R
Get PDFLandslides were addressed as one of the natural hazards that can create extensive disasters. Effective assessment to locate potential landslide events is crucial for planning and risk mitigation. This study, which is located in the Sumitro watershed, Kulon Progo, Yogyakarta, presents a novel approach to landslide susceptibility assessment by integrating the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS) with the Flow-R model. Five key parameters, namely slope, soil properties, groundwater level, soil thickness, and rainfall, were used to create the landslide susceptibility zonation. TRIGRS was used to identify the landslide initiation, while Flow-R was used to create the run-out area. The result was then validated through statistical evaluation using Area Under Curve (AUC) based on the landslide inventory. Results show that landslide susceptibility zonation created from TRIGRS alone resulted in an AUC value of 0.679, while the combination of TRIGRS-Flow-R susceptibility zonation shows a better AUC value of 0.728. The increase of the AUC value of almost 0.05 has enhanced the correlation between the landslide susceptibility zonation and landslide inventory from "acceptable” to "excellent” correlation. This result demonstrates that integrating Flow-R with TRIGRS improves the performance of landslide susceptibility zonation. This study offers a new perspective on creating landslide susceptibility zonation by combining two methods, yielding more reliable results. Doi: 10.28991/CEJ-2025-011-03-020 Full Text: PD
Stability Analysis of Dam with Asphalt Core in Static and Pseudo-Static Conditions
Get PDFManikin Dam was constructed to address the issue of raw water shortage in Kupang Regency and Kupang City. However, there were challenges due to clay materials that did not meet the required specifications. Therefore, this study aimed to use asphalt core design as an alternative by analyzing the stability of the embankment body under both static and pseudo-static conditions. To achieve the aim, the Bishop method was applied using the GeoStudio SLOPE/W application, along with manual calculations. The results showed that the safety factor (SF) at the end of construction without seismic loads met the minimum value of 1.300. Under various water level conditions (FWL, NWL, LWL), SF consistently met the minimum required value of 1.500. Furthermore, the seismic analysis considered both operational base earthquakes (OBE) with a return period of 100 years and maximum design earthquakes (MDE), which had a return period of 5,000 years. Even under OBE and MDE seismic loading conditions, SF exceeded the minimum required value. This implied that the use of an asphalt core could be considered safe in terms of preventing potential landslides under both static and pseudo-static conditions. Based on this outcome, asphalt core became a practical alternative for future dam construction, particularly in areas where clay could be scarce or unstable for technical reasons