2031 research outputs found
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Modeling Tourist Transportation Mode Choice and Trip Chains Through Key Influencing Factors
Get PDFTo understand tourist behavior and the factors influencing it, a thorough analysis of transportation mode choice and trip chain is required, especially from the tourists' perspective. Therefore, this study aims to model transportation mode choice and trip chain in the Bira Peninsula, Bulukumba Regency, South Sulawesi. To achieve this, a quantitative method was employed with a sample size of 500 tourists. The study results show that independent variables significantly impact dependent variables, such as individual characteristics, movement characteristics, destination attributes, mode choice attributes, and trip chains. Several indicators showed a significant influence for transportation mode choice with a confidence level of over 85%. These indicators include age, income, origin location, destination location, number of visits, group size, flexibility, facilities, ease of access, activity type, cost, distance, time, and safety. Similarly, the analysis identified several key indicators affecting the trip chain, with a significance level above 85%. These indicators include age, income, origin, destination location, estimated arrival time, number of visits, flexibility, and destination attraction. Other indicators include facilities, ease of access, trip purpose, activity type, travel time, distance from the city center to the tourist destination, cost, distance, time, and safety. Two significant indicators found as differentiators from previous research are flexibility and type of activity. The study demonstrated high accuracy for the mode choice model and the trip chain model, with validity rates of 98.40% and 97.65%, respectively. The findings indicate that the model accurately captures the factors influencing transportation mode choice and trip chains, making it a valuable reference for future explorations to improve transportation systems' efficiency and comfort. Doi: 10.28991/CEJ-2025-011-02-017 Full Text: PD
Experimental Assessment of Ground Thermal Properties for Shallow Geothermal Energy
No full textGeothermal energy, being clean and renewable on both large and small scales, has become a field of interest for researchers in several areas such as cooling-heating systems, geothermal piles, and geothermal electricity. The purpose of this study is to explore the ground thermal behavior and relevant thermal soil properties for key regions in Jordan. These regions represent either major cities or areas with optimal seasonal temperature variations suitable for such applications. Three key locations were investigated: Tabarbour-Amman, Shafa-Badran-Amman, and Mafraq. Geotechnical soil investigations were conducted using hollow stem auger drilling, with soil samples collected at each meter of depth. Each sample was tested in the laboratory for thermal diffusivity, heat capacity, specific heat, and thermal conductivity. Additionally, thermocouples were installed in each borehole, and the holes were backfilled with the soil cuttings produced during drilling. Seasonal temperature profiles were developed for each site based on the measurements from the thermocouples. Temperature variations were also analyzed using the measured thermal soil properties within a mathematical heat transfer model, with results showing good agreement with the recorded measurements. Thermal diffusivity ranged from 0.315 to 0.365 mm²/s near the ground surface, and from 0.135 to 0.257 mm²/s at a depth of six meters. Thermal conductivity ranged from 0.197 to 0.351 W/m·K near the surface to 0.468 to 0.875 W/m·K at six meters depth. Ground temperature varied from a maximum during the hot season at the surface to a minimum during the cold season at six meters depth. The extreme temperature difference (4.4 to 5.25 °C), along with the observed values of diffusivity and heat capacity, indicates significant potential for energy extraction in the form of heat, in a cost-effective and time-efficient manner
A Comparative Study of Terrestrial Laser Scanning and Photogrammetry: Accuracy and Applications
Get PDFThis study presents a comprehensive comparative analysis of Terrestrial Laser Scanning (TLS) and Digital Close-Range Photogrammetry (DCRP) against traditional Total Station (TS) methods for 3D spatial documentation across a range from 8.00 meters to 2.00 mm. The analysis was conducted through three scenarios: Ground Control Points (GCPs), the Kafrelsheikh University Mosque, and Kafr El Sheikh Tanta Road. Paired t-tests and ANOVA revealed statistically significant differences (p < 0.05) across all variables, with TLS demonstrating superior precision. TLS deviations in linear distance measurements were as low as 2 mm compared to TS, while DCRP exhibited variations ranging from 0.02 m to 0.30 m depending on surface reflectivity and distance. Pearson correlation coefficients exceeded 0.95 for TLS across all axes (X, Y, Z), highlighting its reliability. DCRP, while slightly less consistent, showed minor variability, particularly in the Z-axis. For road crack measurements, TLS captured lengths ranging from 180 mm to 750 mm (mean = 501.417 mm, SD = 207.341 mm), which aligned closely with DCRP results (mean = 504.867 mm, SD = 204.455 mm). The mosque's complex geometry showcased TLS's higher precision (ANOVA F = 15.78, p = 0.0001 for the Y-axis), while DCRP provided faster data acquisition and reduced costs. Both methods demonstrated significant statistical alignment, though TLS consistently outperformed DCRP in accuracy, especially for intricate structures requiring high precision. The findings emphasize the complementary strengths of TLS and DCRP, recommending their integration to achieve an optimal balance of accuracy, efficiency, and cost-effectiveness. Future research should focus on improving the precision of DCRP for detailed architectural and structural documentation while exploring hybrid techniques to enhance the reliability and scalability of 3D surveying methods. Doi: 10.28991/CEJ-2025-011-03-021 Full Text: PD
Mechanical and Physical Evaluations of Fine Sand-RAP Blends for Subgrade and Subbase Applications
Get PDFFine sand has a low load-bearing capacity and tends to deform easily, limiting its use in road construction. Recycled asphalt pavement (RAP) may offer a sustainable solution to improve these properties. Accordingly, the primary objective is to assess how varying RAP content affects the gradation, compaction, bearing capacity, and California Bearing Ratio (CBR) of sand-RAP blends. RAP contents ranged from 0% to 100% by weight. The results show that integrating RAP improves sand gradation, making it suitable for subgrade layers, with mixtures containing 40%-60% RAP meeting subbase requirements. CBR increases significantly with RAP, from 8.78% in fine sand to 41.67% at 100% RAP. Dry density also improves by 12%-16% with 40%-60% RAP, while optimum moisture content (OMC) decreases by over 30%. Bearing capacity increases significantly with RAP content. At 40%-60% RAP, increases range from 299.53% to 411.83% (Dr = 60%) and 243.69% to 318.43% (Dr = 90%). RAP inclusion enhances stiffness, peaking at 530% (Dr = 60%) and 326% (Dr= 90%) between 40%-60% RAP. Initial gains are steady at 10%-30% RAP, but diminishing returns occur beyond 50% RAP. Generally, notable performance is achieved at 40% RAP, while 50% RAP ensures optimal stiffness and structural integrity, with diminishing returns afterward. Doi: 10.28991/CEJ-2025-011-05-017 Full Text: PD
The Role of Urban Structure in Enhancing the Sustainability of Cities: A Comparative Study
Get PDFThe topic of sustainable urban structure is a crucial area in urban planning, given its direct connection to land use patterns, their distribution and density, as well as their relationship with transportation network patterns. These factors play a vital role in achieving sustainability. The theoretical aspect of the research focused on modern literature and global experiences addressing sustainable urban structures, aiming to provide a clear definition and to identify critical indicators that influence the achievement of urban sustainability. The study identified seven key indicators: density, average distance to the center, hierarchical structure, spread index, land price, the location of the center relative to the city, and the street network pattern. These indicators are applicable and measurable for any city worldwide to assess the sustainability of its urban structure. The research conducted a comparative case study between the cities of Kut and Hillah. The urban structure of Kut is characterized by separation due to the presence of a river, whereas Hillah features a more connected structure, in addition to differences in density distribution, land use, and transportation network patterns. The indicators for both cities were measured using mathematical models, geographic information systems (GIS), and three-dimensional spatial representations. The study concluded that while the indicator results varied between the two cities, Kut achieved better outcomes than Hillah in four of the seven indicators. Doi: 10.28991/CEJ-2025-011-04-015 Full Text: PD
Bond Strength Evaluation of Waterproofing Membrane Assembly in Concrete Bridges
Get PDFOn the concrete bridge decks overlaid by HMA, slippage cracks usually appear on the HMA layer because of the presence of waterproofing membranes below the HMA layer and a lack of bonding of the membrane with the PCC underlying layer. The objective of this work is to develop a laboratory-based method for the fabrication of test samples of an HMA layer, waterproofing membrane, and PCC layer system. In addition, a bond strength test procedure was adapted to evaluate the bonding of the three layers assembly at different test temperatures in the laboratory prior to the field application. According to the obtained evaluation results, it was found that the weakest bond in the HMA, waterproofing membrane, and PCC assembly is the bond between the HMA layer and the waterproofing membrane. The bond strength of the assembly is highly affected by increasing temperature, since it lost approximately 75% of its strength when the test temperature increased from 25°C to 50°C. Likewise, as the test temperature increased from 25°C to 60°C, the assembly lost approximately 75% of its strength. Therefore, the bond strength should be evaluated at the expected pavement temperature in the field, specifically at the membrane interface level. Doi: 10.28991/CEJ-2025-011-02-010 Full Text: PD
An Assessment of Nature-Based Solutions Water Infrastructure for Flood Risk Reduction in Unplanned Area
Get PDFThis study aimed to investigate the effectiveness of the Brigif Reservoir as a pioneering nature-based infrastructure solution for mitigating flood risk in the Kemang area, a significant business district in South Jakarta, and to provide a potential raw water supply for city parks. We employed coupled HEC-HMS and HEC-RAS 1D2D unsteady flow models to analyze rainfall-runoff and flood regimes before and after basin intervention in a rainfall scenario from January 2020. The flood model demonstrated highly satisfactory performance on the calibration results, as evidenced by an NSE value of 0.93 on a scale of 0 to 1. Flood risk was defined using the flood hazard, vulnerability, and capacity indices, and ArcGIS and QGIS were used to prepare and visualize the output after the model performance was qualified. The study revealed that the Brigif Reservoir could reduce the peak discharge of the January 2020 flood in the Kemang area by 19% while decreasing the risk of high-level flooding by 12%. The Brigif Reservoir, as a Nature-Based Solution (NBS) infrastructure, retains approximately 250,000 m³ of flood discharge, which can be utilized by the local government for watering gardens and as potential raw water for residents because it meets the national surface water quality standards in dry conditions and requires additional treatment during the wet season. The potential for groundwater recharge was estimated to be approximately 250 m³ and 6000 m³ for one hour and one day, respectively. For future studies, it is recommended to develop non-structural actions, such as a flood early warning system incorporating machine learning that could potentially support the operational performance of the NBS infrastructure. This study proposes the implementation of a series of sustainable infrastructure solutions, including rooftop storage, underground storage, and underground retention systems, at the building scale within each sub-catchment to mitigate flood risk levels in the Kemang region from high to acceptable levels. The findings of this research will be of significant value to the Water Resources Agency in evaluating the potential application of NBS infrastructure for flood mitigation and adaptation strategies and programs in response to the impacts of global climate change. Doi: 10.28991/CEJ-2025-011-05-07 Full Text: PD
Impact of Wind Turbine Distraction on Crash Severity: Assessment and Prediction Study
Get PDFWind turbines are increasingly installed near highways, yet their potential role as external distractions impacting traffic crashes remains underexplored. This study investigates the effect of wind turbines on crash severity and frequency along Jordan's King's Highway, analyzing data through a mixed-effect logit model and machine learning techniques. Key factors, including driver demographics, road geometry, and environmental conditions, were incorporated to provide a comprehensive analysis. The findings indicate a 117.4% increase in severe injury crashes (KAB) and a 25.7% rise in property damage only near wind turbines. Using MK models such as Bagged Tree classifiers and SMOT-balanced datasets, the study achieved a high prediction accuracy of 89.6% for crash severity. Shapley value analysis identified crash type and wind turbine proximity as critical predictors, while other influential factors included younger drivers, poorly separated roads, and higher speed limits. By integrating statistical and ML approaches, this research provides actionable insights into the relationship between wind turbines and road safety. The results underscore the need for regulatory policies to optimize wind turbine placement and reduce their potential as driver distractions. This study also demonstrated the potential of ML techniques to enhance traffic safety analysis, paving the way for future research to address multi-class crash severity predictions and other external roadside distractions. Doi: 10.28991/CEJ-2025-011-04-018 Full Text: PD
Enhancing Operating Rules for Water Pumping Stations Under Transient Flow Conditions by Using Surge Tanks
Get PDFWith an emphasis on Pump Station 1 (PS1) of the Basra Water Project (Open Canal) in Iraq, this study examines the essential hydraulic parameters of water pumping stations under transient flow situations. The study assesses the effects of routine operations, unexpected shutdowns, and surge tank installations on pressure stability and system flexibility using hydraulic modeling with HAMMER V8i. The findings show notable changes in pressure during brief occurrences. An abrupt shutdown without surge tank protection resulted in minimum pressures of 12.5 m in pipes L1 and L2, exposing them to hydraulic transient effects. The maximum pipe pressure under normal circumstances was 17.5 m (L3). Because of its exposure to low-pressure occurrences, the analysis identifies L1 as the most in-danger pipeline. It has been demonstrated that traditional operating procedures, which frequently ignore transient dynamics, increase the probability of service disruption and lead to inefficiency. In contrast, adding surge tanks reduces pressure variability and lessens the impacts of the water hammer, significantly increasing pressure stability, especially when three tanks are used. The results highlight how adaptable operational procedures are essential for employing and managing water delivery systems. According to the study findings, adding surge tanks improves durability and performance while lowering the risks of transient flow occurrences. This offers a guide for restructuring water pumping station operations
WatAI: AI-Based System for Real-Time Flow Monitoring and Demand Prediction in Water Networks
Get PDFEfficient monitoring and control of water demand are crucial for sustainable water resource management. Bogotá, Colombia, currently faces supply rationing due to climate change and ineffective public policies. This study presents WatAI (Water + AI), an AI-powered system designed for real-time flow monitoring and demand prediction in water distribution networks. The system integrates flow sensors, microcontrollers, and machine learning algorithms to capture high-resolution temporal data. A dynamic sequential artificial neural network (ANN) with ReLU activation and Adam optimization is implemented, allowing real-time adjustments (1 sec) to flow variations and anomaly detection. To enhance accuracy, the system applies real-time signal filtering and transmits early alerts via email to service providers. The ANN model achieved an MSE of 0.006510, demonstrating improved accuracy with increasing historical data. Compared to traditional forecasting models, WatAI provides higher temporal resolution and adaptability to demand fluctuations, making it a more effective tool for intelligent water management. The study contributes to the development of IoT-based smart infrastructures for sustainable urban water planning