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Evaluating Groundwater Quality Using Multivariate Statistical Analysis and Groundwater Quality Index
No full textUnder pressure from surface water pollution and climate change, groundwater becomes a critical water source. Information on groundwater quality could contribute to effective groundwater management. This study was carried out to utilize multivariate statistical analysis and the groundwater quality index (GWQI) to evaluate groundwater quality in Ca Mau Province, Vietnam. Twenty-five groundwater samples from residential-urban areas, cemetery areas, landfill areas, and saline intrusion areas were collected for this study. Groundwater quality was evaluated using the National Technical Regulation on Groundwater Quality (QCVN 09-MT:2015/BTNMT) and GWQI. Principal component analysis (PCA) was used to identify potential polluting sources and key variables influencing groundwater quality. Cluster analysis (CA) was applied to cluster groundwater quality, and the sites were recommended for future monitoring. The results revealed that NH4+-N contaminated groundwater in the landfill area, while the saline intrusion area was polluted by TDS and NH4+-N. The groundwater quality classified as excellent, good, poor, and very poor accounted for 44, 40%, 12%, and 4%, respectively. Cluster analysis divided groundwater quality into four groups, mainly based on the presence of NH4+-N and TDS. Nine groundwater sampling locations could be removed from the current groundwater quality program but still ensuring representativeness as a result of CA. PCA proposed two main sources of variation in groundwater quality at each residential-urban area: the cemetery area, the landfilling area, and the saline intrusion area. The groundwater parameters (i.e., pH, TDS, permanganate index, NH4+-N, NO3--N, and Fe) should be continued to monitor. Domestic and industrial wastewater discharge, leachate from cemeteries and landfills, the nature of groundwater aquifers, and seawater intrusion could be potential sources of groundwater variation. The current findings provide scientific information for local environmental authorities to manage and monitor groundwater quality in the study area. Doi: 10.28991/CEJ-2024-010-03-03 Full Text: PD
Effect of Infilled Frames on Reduction Factor (R) for RC Irregular Structure
No full textInvestigating the modification factors as a critical seismic design tool, delineating the anticipated level of inelastic behavior within structural systems during seismic events. Both damping and ductility are included in this factor, particularly at movement nearing maximum capacity. Moreover, it offers valuable insights into buildings' response during earthquakes and the anticipated behavior of structures compliant with building codes during design earthquakes. Essentially, it mirrors the structure's capacity to dissipate energy via an inelastic mechanism. In this research, the infill (RC) structures with various structural irregularities were focused. The selected irregularities included dimension, elevation, and mass. Infill location, number of bays, and seismic zone were the expected R factors for RC frames. Non-linear static pushover analysis was adopted in numerical simulation. The available data gathered from the literature was used to validate the outcomes of the developed models. Additionally, the effects of different types of soil were taken into consideration, and the research results demonstrated that the value of the modification factor (R) for change in stiffness and mass of high-rise buildings for bare and infill (RC) structures is less compared to irregular (RC) structures. It was concluded that the same structure with different types of soil and different parameters has a great effect on the value of R for bare and infill regular and irregular (RC) structures. Furthermore, recommendations for accurate R estimation for RC structures were discussed. Doi: 10.28991/CEJ-2024-010-08-09 Full Text: PD
Role of Slag Replacement on Strength Enhancement of One-Part High-Calcium Fly Ash Geopolymer
No full textThis paper reports the effect of slag (SL) replacement and water-to-binder (w/b) ratio on properties of one-part geopolymer derived from high-calcium fly ash (FA) and sodium silicate powder (NP). The FA was replaced by SL at the rates of 20% and 40%, respectively. This study focused on conducting experimental tests to evaluate the relative slump, setting time, compressive strength, and flexural strength of one-part FA-based geopolymer. The relationship between compressive and flexural strengths of one-part geopolymer mortar was expressed using the simplified linear regression model, whereas the normalization of compressive and flexural strengths with SL replacement by the strength of one-part geopolymer mortar without SL as the divisor was also evaluated. Experimental results showed that the increase of SL replacement and w/b ratio significantly affected the workability and strength development of one-part geopolymer mortar. Higher SL replacement exhibited a positive effect on their compressive and flexural strengths; however, a reduction in its setting time was obtained. The enhancement in strength development of one-part geopolymer was primarily due to the increased calcium content of SL. Similarly, reducing the w/b ratio in the production of one-part geopolymer resulted in a decrease in setting time and an increase in strength development. Based on the relationship between compressive and flexural strengths, the prediction coefficient value (R2) obtained from the curve fitting procedure was 0.835, indicating a good level of reliability and acceptability for engineering applications. Doi: 10.28991/CEJ-SP2024-010-013 Full Text: PD
The Application of Neural Networks to Predict the Water Evaporation Percentage and the Plastic Shrinkage Size of Self-Compacting Concrete Structure
No full textThis article presents a solution using an artificial neural network and a neuro-fuzzy network to predict the rate of water evaporation and the size of the shrinkage of a self-compacting concrete mixture based on the concrete mixture parameters and the environment parameters. The concrete samples were mixed and measured at four different environmental conditions (i.e., humid, dry, hot with high humidity, and hot with low humidity), and two curing styles for the self-compacting concrete were measured. Data were collected for each sample at the time of mixing and pouring and every 60 minutes for the next ten hours to help create prediction models for the required parameters. A total of 528 samples were collected to create the training and testing data sets. The study proposed to use the classic Multi-Layer Perceptron and the modified Takaga-Sugeno-Kang neuro-fuzzy network to estimate the water evaporation rate and the shrinkage size of the concrete sample when using four inputs: the concrete water-to-binder ratio, environment temperature, relative humidity, and the time after pouring the concrete into the mold. Real-field experiments and numerical computations have shown that both of the models are good as parameter predictors, where low errors can be achieved. Both proposed networks achieved for testing results R2 bigger than 0.98, the mean of squared errors for water evaporation percentage was less than 1.43%, and the mean of squared errors for shrinkage sizes was less than 0.105 mm/m. The computation requirements of the two models in testing mode are also low, which can allow their easy use in practical applications. Doi: 10.28991/CEJ-2024-010-01-07 Full Text: PD
Unveiling Effectiveness of Lean Construction Practices: A Comprehensive Study through Surveys and Case Studies
No full textConstruction projects frequently encounter challenges such as stagnant productivity, excessive waste, cost overruns, and delays, contributing to sustainability issues. In response to these issues, Lean Construction (LC) has emerged as a methodology aimed at eradicating inefficiencies and wasteful practices. However, the construction industry has been slow to embrace LC, primarily due to a lack of comprehensive evaluations regarding its real-world effectiveness. This study seeks to thoroughly assess the effectiveness of LC when implemented in construction projects in Pakistan. The research involved conducting a survey among experts in the construction industry, utilizing a comprehensive questionnaire to evaluate the extent of LC adoption and its impact on construction project performance. The collected data underwent rigorous statistical analysis to ascertain the influence of LC practices on project outcomes. To validate the survey results, the study selected five case study projects for in-depth analysis. These case studies assessed how well the projects adhered to LC principles and examined the resulting effects on project delays, cost overruns, quality issues, rework, and health-related concerns. The findings consistently confirmed that a higher level of adherence to LC principles led to significant reductions in project delays, cost overruns, quality issues, and health-related problems. This analysis strongly supports the notion that a more extensive adoption of LC practices results in substantial improvements in project performance. By presenting these compelling results, this study offers valuable insights to the construction industry, providing a clearer path for the effective integration of LC practices. Doi: 10.28991/CEJ-2024-010-04-09 Full Text: PD
Effect of Steel Fiber on Plastic Hinge Length of Concrete Columns: Buckingham Theory Application
No full textThe accuracy of designing the performance of concrete structures nowadays not only depends on the use of standard materials (cement, sand, and gravel) for certain concrete strengths but also on the accuracy of using additional materials for concrete, such as steel fiber. The use of steel fiber not only can improve the performance of concrete structures to behave in a ductile manner but can also form plastic hinges according to design purposes. The design of the axial load of Pa=0.121.Ag.f'c is based on the prediction of the column's axial capacity. The columns were designed to behave in a flexural manner. As predicted, the lengths of the plastic hinges were found not too long. Controlling the length of plastic hinges in the design of structural concrete members is necessary to avoid excessive displacements. The control is mainly related to the prediction of the plastic hinge length. Thus, in this case, a plastic hinge length formula is required. In the study, the length of the plastic hinges of columns, which are confined with square stirrups and reinforced with steel fiber with Vf = 0%, 0.5%, 1%, 1.5%, and 2%, is proposed. This plastic hinge length formula is proposed after all column test specimens have met the displacement ductility requirement of mD>4, meaning that all test specimens are defined as very ductile. Doi: 10.28991/CEJ-2024-010-05-03 Full Text: PD
Analysis of Traffic Safety Factors and Their Impact Using Machine Learning Algorithms
No full textThe safety of road traffic is facing increasing challenges from a range of factors, and this study aims to address this issue. The paper describes the development of a model that assesses both the quantitative and qualitative aspects of the current traffic situation and can also predict future trends based on monthly data on traffic accidents over a period of years. The dataset is composed of the number of accidents that occurred in the Pristina region over a 10-year period, and these are categorized based on the type of accident and safety factors, including human, vehicle, and road factors. By using machine learning algorithms, a model has been developed that determines the factor with the greatest impact on traffic safety. To create the model, the algorithms Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Random Trees (RT) were used. The model evaluates the contribution of human, road, and vehicle factors to traffic accidents, using machine learning algorithms and 36 types of traffic accidents to analyze the relevant statistics. The results indicate a very good fit of the model according to the MLR algorithm, and this model also identifies the road factor as the main influencer of the traffic safety level. Doi: 10.28991/CEJ-2024-010-09-06 Full Text: PD
Properties and Structure of Functional Concrete Mixtures Modified with River Shell Powder
No full textThe recycling of the aquaculture waste into clam powder reduces solid emissions and natural resources, which is important for Portland cement production. This study determines the feasibility of using recycled river shell waste as a partial replacement for cement in concrete technology. The study used normative methods and optical microscopy; the properties of cement mixtures, such as normal consistency, setting time (ST), compressive and flexural strength, were studied. Research findings have shown that the inclusion of river shell powder (RSP) in cement mixtures can reduce water demand and a decrease in setting time with increasing RSP content. It was also found that the strength of the cement mixture can be maintained with an RSP content of up to 10%. The following properties of the concrete were determined: workability, compressive strength (CS), and water absorption. Using RSP as a partial replacement for cement has been proven to elevate the slump of the fresh concrete cone. CS is maintained at a level comparable to the control composition, with an RSP content of no more than 8%, and water-absorbing is reduced by 7.31%. This study created new compositions, and the links between the ingredients, properties, and structure of cement composites modified with river shell powder were investigated. Additionally, the properties of the structure-formation process of these modified composites were studied. Doi: 10.28991/CEJ-2024-010-07-020 Full Text: PD
Measurement Model for Determining the Disparity Factors of Intercity Railway Transportation
No full textCountries that are still developing experience significant disparities in access to railway services, as these nations also grapple with societal inequality issues that remain unaddressed. In developed countries, railway transportation systems serve as the primary mode of transportation for both passengers and goods. However, in recent years, studies on disparities in developed countries have increased, while literature concerning developing countries remains scarce. Therefore, this study takes place in Thailand, a developing country facing significant population disparities. The objective is to examine factors contributing to these disparities in access to railway transportation systems across cities, using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify user disparities. The sample group comprises 1,252 randomly selected railway users from various regions in Thailand, obtained through Stratified Random Sampling. The results reveal seven dimensions of disparities: cultural, spatial, societal, political, knowledge-based, economic, and environmental. The CFA results also highlight cultural disparities as a significant factor in explaining access disparities among railway users. These findings can inform relevant organizations, aiding them in better understanding the actual needs of railway users and aligning railway development plans accordingly. Ultimately, this contributes to policy development aimed at reducing access disparities and fostering a more equitable society. Doi: 10.28991/CEJ-2024-010-03-01 Full Text: PD
Modelling of Flood Hazard Early Warning Group Decision Support System
No full textEarly warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PD