Ksce Journal of Civil Engineering
Springer Science+Business Media
About: Ksce Journal of Civil Engineering is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Compressive strength & Geotechnical engineering. It has an ISSN identifier of 1226-7988. Over the lifetime, 4801 publications have been published receiving 48613 citations.
Topics: Compressive strength, Geotechnical engineering, Materials science, Finite element method, Cement
Papers published on a yearly basis
TL;DR: In this paper, the authors employed a questionnaire survey to elicit the causes of this situation by interviewing 87 Vietnamese construction experts and found that there are no differences in the viewpoints between three principal parties in the project.
Abstract: In-planned duration and cost at project closing are the two of criteria of successful project and successful project management. In Vietnam, regularly, construction projects have met delays and cost overruns. This research has employed a questionnaire survey to elicit the causes of this situation by interviewing 87 Vietnamese construction experts. Twenty one causes of delay and cost overruns appropriate with building and industrial construction project were inferred and ranked with respect to frequency, severity and importance indices. Spearman’s rank correlation tests showed that there are no differences in the viewpoints between three principal parties in the project. A comparison of causes of time and cost overruns was done with various selected construction industries in Asia and Africa. Factor analysis technique was applied to categorize the causes, which yielded 7 factors: Slowness and Lack of constraint; Incompetence; Design; Market and Estimate; Financial capability; Government; and Worker. These findings might encourage practitioners to focus on delay and cost overruns problem that might have existed in their present or future projects.
TL;DR: In this article, the authors used MATLAB software and three-layer perceptron network for modeling and estimation of nitrate pollution in groundwater of marginal area of Zayandeh-rood River, Isfahan, Iran, using water quality and artificial neural networks.
Abstract: Excessive use of chemical fertilizers, especially nitrogen fertilizers to increase crop and improper purification, and delivery of municipal and industrial wastewater are proposed as factors that increase the amount of nitrate in groundwater in this area. Thus, investigation of nitrate contamination as one of the most important environmental problems in groundwater is necessary. In the present study, modeling and estimation of nitrate pollution in groundwater of marginal area of Zayandeh-rood River, Isfahan, Iran, was investigated using water quality and artificial neural networks. 100 wells (77 agriculture well, 13 drinking well and 10 gardens well) in the marginal area of Zayandeh-rood River, Isfahan, Iran were selected. MATLAB software and three-layer Perceptron network were used. The back-propagation learning rule and sigmoid activation function were applied for the training process. After frequent experiments, a network with one hidden layer and 19 neurons make the least error in the process of network training, testing and validation. ANN models can be applied for the investigation of water quality parameters.
TL;DR: In this article, the authors validate global terrestrial MODIS ET in Asia, showing that a MODIS global terrestrial ET product can estimate actual evapotranspiration (ET) with reasonable accuracy.
Abstract: Evapotranspiration (ET), or the sum of water released to the atmosphere from ground surfaces, intercepts canopy precipitation through evaporation and plant transpiration and is one of the most significant components in the water cycle. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) 16 global terrestrial ET products were validated at 17 flux tower locations in Asia. Overall, overestimations due to energy balance misclosure distorted the trend of the data at nine locations [r: 0.27–0.82; bias: −21.41–2.38 mm 8-d−1; Root Mean Square Error (RMSE): 6.12–21.81 mm 8-d−1]. Regardless of variation in the scattering patterns, good agreements between MODIS-based ET and ET measured at the flux towers were observed at five locations (r: 0.50–0.76; bias: −1.42–1.99 mm 8-d−1; RMSE: 1.99–8.96 mm 8-d−1). Underestimation at one site (r = 0.28, bias = −17.00 mm 8-d−1, RMSE = 17.41 mm 8-d−1) was accompanied by mismatches at two sites (r = 0.12–0.18; bias = −4.19 — −0.04 mm 8-d−1, RMSE = 5.76–7.66 mm 8-d−1). The best performances of the MOD16 ET algorithm were observed at sites with forested land cover, but no substantial differences were found under a variety of climate conditions. This study is the first comprehensive trial to validate global terrestrial MODIS ET in Asia, showing that a MODIS global terrestrial ET product can estimate actual ET with reasonable accuracy. We believe that our results can be used as baseline ET values for satellite image-based ET mapping research in South Korea.
TL;DR: Three optimization-algorithm based support vector machines for damage detection exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods, and the genetic algorithm based SVM had a better prediction than other methods.
Abstract: Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.
TL;DR: In this paper, a species of Bacillus group, B. megaterium was used to trigger the calcite precipitation, and the results showed that MICP could effectively improve shear strength and reduce hydraulic conductivity for both residual soil and sand.
Abstract: Microbial-Induced Calcite Precipitation (MICP) has recently emerged as a sustainable technique for soil improvement. This paper aims to study the effectiveness of MICP in improving the shear strength and reducing the hydraulic conductivity of soils. A species of Bacillus group, B. megaterium was used to trigger the calcite precipitation. The experimental variables included soil types (tropical residual soil and sand), soil densities (85%, 90%, and 95% of their respective maximum densities), and treatment conditions (untreated, treated with cementation reagents only, treated with B. megaterium only, and treated with B. megaterium and cementation reagents). The results showed that MICP could effectively improve shear strength and reduce hydraulic conductivity for both residual soil and sand. The improvements, however, varied with soil densities, soil types, and treatment conditions. With MICP treatment, the improvement ratios in shear strength of the residual soil specimens were significantly higher (1.41–2.64) than those of the sand specimens (1.14–1.25). On the contrary, the sand specimens resulted in greater hydraulic conductivity reduction ratios (0.09–0.15) than those of the residual soil specimens (0.26–0.45). These observations can be explained by the particle-particle contacts per unit volume and pore spaces in the soil specimens. Both soil specimens when treated with cementation reagents only exhibited slight alterations in the shear strength (ranging from 1.06–1.33) and hydraulic conductivity (ranging from 0.69–0.95). The results implied that natural calcite forming microorganisms only exist for insignificant amount. The amount of calcite precipitated in the treated residual soil specimens ranged from 1.080% to 1.889%. The increments of calcite content in the treated sand specimens were comparatively higher, ranging from 2.661% to 6.102%. The results from Scanning Electron Microscope (SEM) analysis confirmed the experimental findings.