Showing papers in "Ksce Journal of Civil Engineering in 2018"

Prediction of Water Quality Parameters Using ANFIS Optimized by Intelligence Algorithms (Case Study: Gorganrood River)

Abstract: Water quality management and control has high importance in planning and developing of water resources. This study investigated application of Genetic Algorithm (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolution (DE) in improving the performance of adaptive neuro-fuzzy inference system (ANFIS), for evaluating the quality parameters of Gorganroud River water, such as Electrical Conductivity (EC), Sodium Absorption Ratio (SAR) and Total Hardness (TH). Accordingly, initially most suitable inputs were estimated for every model using sensitivity analysis and then all of the quality parameters were predicted using mentioned models. Investigations showed that for predicting EC and TH in test stage, ANFIS-DE with R2 values of 0.98 and 0.97, respectively and RMSE values of 73.03 and 49.55 and also MAPE values of 5.16 and 9.55, respectively were the most appropriate models. Also, ANFIS-DE and ANFIS-GA models had the best performance in prediction of SAR (R2 = 0.95, 0.91; RMSE = 0.43, 0.37 and MAPE = 13.43, 13.72) in test stage. It is noteworthy that ANFIS showed the best performance in prediction of all mentioned water quality parameters in training stage. The results indicated the ability of mentioned algorithms in improving the accuracy of ANFIS for predicting the quality parameters of river water.

An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA

Abstract: The traffic flow prediction plays a key role in modern Intelligent Transportation Systems (ITS). Although great achievements have been made in traffic flow prediction, it is still a challenge to improve the prediction accuracy and reduce the operation time simultaneously. In this paper, we proposed a hybrid prediction methodology combined with improved seasonal autoregressive integrated moving average (ISARIMA) model and multi-input autoregressive (AR) model by genetic algorithm (GA) optimization. Since traffic flow data has strong spatio-temporal correlation with neighboring stations, GA is used to select those stations which are highly correlated with the prediction station. The ISARIMA model is used to predict the traffic flow in test station at first. A multiinput AR model with traffic flow data in optimal selected stations is built to predict the traffic flow in test station as well. The final prediction result can be gained by combining with the results of ISARIMA and multi-input AR model. The test results from traffic data provided by TDRL at UMD Data Center demonstrate that proposed algorithm has almost the same prediction accuracy with artificial neural networks (ANNS). However, its operation time is almost the same with SARIMA model. It is proved to be an effective method to perform traffic flow prediction.

Prediction of Slope Stability using Naive Bayes Classifier

Abstract: Slope stability prediction is of primary concern in identifying terrain that is susceptible to landslides and mitigating the damages caused by landslides. In this study, a Naive Bayes Classifier (NBC) was employed to predict slope stability for a slope subjected to circular failures, based on six input factors: slope height (H), slope angle (α), cohesion (c), friction angle (φ), unit weight (γ), and pore pressure ratio (r u ). An expectation maximization algorithm was used to perform parameter learning for the NBC with an incomplete data set of 69 slope cases. The model validation with 13 new cases shows that, when compared to the existing empirical approach, the proposed NBC model yields better performance in terms of both accuracy and applicability (i.e., the NBC allows us to determine the probability of slope stability based on any subset of the six input factors).

Mechanical Properties, Thermal Conductivity, and Sound Absorption of Pervious Concrete Containing Recycled Concrete and Bottom Ash Aggregates

Abstract: This paper presents the effect of replacement level of natural aggregate (crushed limestone aggregate, LS) with Recycled Concrete (RC) and coal Bottom Ash aggregates (BA) on pervious concrete properties. Mechanical properties, thermal conductivity, and sound absorption of pervious concrete were tested. Results showed that the compressive strength of BA pervious concrete was excellent and comparable to that of LS pervious concrete. While the compressive strength of RC pervious concrete was slightly reduced to between 85 and 99% of that of LS pervious concrete. The thermal conductivity and sound absorption of pervious concretes containing RC and BA were significantly improved compared to those of pervious concretes containing conventional LS.

Enhancement the Properties of Sugar Cane Bagasse Ash with High Carbon Content by a Controlled Re-calcination Process

Abstract: In this study, the feasibility and effectiveness of a controlled laboratory re-calcination process was evaluated in order to mitigate the negative effects of Sugar Cane Bagasse Ash (SCBA) with high carbon content on hydration and fresh properties of concrete. Measurements of particle size distribution, chemical composition, BET specifc surface area, and pozzolanic activity were realized to characterize the as-received and re-processed SCBA. Moreover, the distinct SCBAs were evaluated based on results of isothermal calorimetry and time of setting by Vicat method in cement-SCBA pastes and compressive strength, Young’s modulus, and water absorption in a 35-MPa concrete. The results showed that the re-calcination process decreased the loss on ignition from 20.9% to 2.1% at laboratory calcination thus increasing the silica content of the ash. Re-burnt SCBA provided the control of setting times and the evolution of the compressive strength of concrete changed with the nature of the used ash with a superior behavior being observed for lab-conditioned re-calcination SCBA.

Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

Abstract: Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combining symbolic regression and Autoregressive Integrated Moving Average Model (ARIMA) was proposed in this paper. It can take unique strength of each single model to capture the complexity patterns beneath data structure. Using the real data from Xi’an metro line 1, the performance of the hybrid model was compared with the ARIMA model and Back Propagation (BP) neural networks. The results show that the hybrid model outperforms other two models. Mean Absolute Percentage Error (MAPE) of hybrid models have an extra 54.24%, 58.98% increase over the BP neural networks and an extra 64.44%, 68.27% increase over the ARIMA models for entrance and exit respectively. In addition, the t-test of MAPE during workday and holiday reflects the hybrid model possesses comparable forecasting ability under different conditions. Moreover, with the increase of the prediction steps, the superiority of the proposed model is more significant.

Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics

Abstract: A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is useful for controlling the plant’s operation. In this paper Auto-Regressive Integrated Moving Average (ARIMA) and Neural Network Auto-Regressive (NNAR) modeling techniques were applied on a WTP’s influent water characteristics time series to make some models for short-term period (to seven days ahead) forecasting. The ARIMA and NNAR models both provided acceptable generalization capability with R2s ranged from 0.44 to 0.91 and 0.45 to 0.92, respectively, for chloride and temperature. Although a more prediction performance was observed for NNAR in comparison with ARIMA for all studied series, the forecasting performance of models was further examined using Time Series Cross-Validation (TSCV) and Diebold-Mariano test. The results showed ARIMA is more accurate than NNAR for forecasting the horizon-daily values for CO2, Cl and Ca time-series. Therefore, despite of the good predictive performance of NNAR, ARIMA may still stands as better alternative for forecasting task of aforementioned series. Thus, as a general rule, not only the predictive performance using R2 statistic but also the forecasting performance of a model using TSCV, are need to be examined and compared for selecting an appropriate forecasting model for WTP’s influent characteristics.

Utilization of recycled tiles and tyres in stabilization of soils and production of construction materials – a state-of-the-art review

Abstract: Tile waste is found in several forms including manufacturing slurry, manufacturing dust, and solid pieces from cracked, smashed, and rejected tiles at the construction sites. Worn out tyres that are no longer safe to be used by vehicles are either discarded or burned, adversely impacting natural ecosystems. These wastes are non-degradable and have a direct environmental impact. Poor waste management can lead to hazardous pollution, reduced soil fertility, and increased space consumption at disposal sites. The massive and increasing volume of the tile and tyre wastes calls for recycling of the materials for economical reuse, cleaner production, and greener development. One area for beneficial reuse of these waste materials is the improvement of engineering properties in soft soil. Structures on soft soils may experience several forms of damage due to insufficient bearing capacity and excessive settlement. Hence, soil stabilization is often necessary to ensure that the soft soil can meet the engineering requirements for stability. A comprehensive review of the published literature on the use of recycled tyres and tiles to stabilize and enhance soft soils was carried out. The properties of soft soil-waste mixtures such as liquid limit, plastic limit, plasticity index, compaction behaviour, unconfined compressive strength, and California Bearing Ratio have been presented. When used as partial replacement of cement, sand, and aggregate in concrete, the effect of tyre and tile waste on workability, durability, and compressive strength of the concrete has also been presented. Recycled tiles and tyres have been used with or without any other admixtures to sustainably improve the strength and bearing capacity of soil. The suitability of recycled tiles and tyres in soil stabilization has been discussed with regard to enhancement of strength and reduction of settlement. In addition, the beneficial effects of the recycled tiles and tyres, when they partially replace cement, sand or stone in concrete, have been discussed.

Comparative Evaluation of Infiltration Models

Abstract: Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R2) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.

An Empirical Study on Performance Measurement Factors for Construction Organizations

Abstract: Like any other organization, it has become essential for the organizations in the construction industry to measure their performance effectively for long-term survival in today’s competitive business environment. Therefore, it is imperative for a construction organization to know about various performance measurement factors to evaluate its performance. However, most of the previous studies have focused on identification of factors for measuring performance at the level of projects only. Moreover, the majority of these studies have been undertaken in context to the developed construction markets. The present study addresses these gaps in the literature by identifying critical factors for examining the performance of construction firms at the organizational level. A total of 20 organizational performance attributes were identified and analyzed using a questionnaire survey conducted on 106 respondents among 90 different organizations operating in the National Capital Region (NCR) of India. It was found that attributes such as timely completion, relationship with the client, and satisfaction (in terms of both product and services) carry more weight than the cost performance of a construction organization. In addition to this, factor analysis conducted on the performance attributes of high importance has resulted in six performance factors: (1) profitability and asset management, (2) satisfaction of key stakeholders, (3) predictability of time and cost, (4) environment, health, and safety (EHS), (5) quality consciousness, and (6) low staff turnover. The performance factors obtained from the study may provide useful guidelines to the construction organizations enabling them to examine and improve their performance.

Identification and Evaluation of Construction Projects’ Critical Success Factors Employing Fuzzy-TOPSIS Approach

Abstract: The construction industry is a significant motive for the economic and industrial developments in countries. Consequently, the success of the construction projects is crucial for any country because the failure of these projects imposes extreme costs to the economic and industrial development of the country. Thus, the identification of the critical success factors of the construction projects and ranking these factors has a major role in the success and failure of the projects. In this study, the critical success factors are determined through the survey of the literature, and the set of critical success factors are considered as options. After that, a questionnaire was prepared with respect to the criteria such as time, cost, quality, and safety, which are the measurements of the success and failure of the projects. This questionnaire was distributed to the professionals and experts of the construction industry that form the statistical population to determine the percentage of approval and importance of each criterion. Then, the Fuzzy TOPSIS multiple criteria decision-making methods have been used to rank the critical success factors of the construction projects. Finally, a comparison of proposed method and Entropy-based Fuzzy Multi-MOORA has been shown. According to the research results, the level of the effect of each critical factor on the successful execution of Iran’s construction projects will be provided.

Safety in green building construction projects in Singapore: Performance, critical issues, and improvement solutions

Abstract: The green buildings have achieved a rapid development recently with the surge in global interest in sustainable development However, the emphasis placed on the issue of safety in green building construction projects remains minimal This study aims to conduct an investigation and comparison of safety performance and critical safety issues between green and conventional building construction projects in Singapore, and to propose a series of feasible solutions to improve the safety performance in green building construction projects To achieve these objectives, a questionnaire survey was conducted, and data collected from 30 construction companies were analyzed The analysis results showed that the accident rate in green building construction projects was higher than that in conventional building construction projects The results also indicated that, although the two types of projects shared the same top ten critical safety issues, six critical safety issues, namely, “exposure to hazardous substances”, “inhalation”, “moving/handling heavy loads”, “respiratory failure”, “being struck against manually operated tools”, and “being struck by falling objects”, were perceived differently between green and conventional building construction projects This study also recommended a set of specific solutions to improve safety performance in green building construction projects, based on the feedback collected from the questionnaire survey

Model Updating in Complex Bridge Structures using Kriging Model Ensemble with Genetic Algorithm

Abstract: Computational cost reduction and the best solution seeking are frequently encountered during model updating for complex structures. In this study, a hybrid algorithm using kriging model and genetic algorithms (GAs) is proposed for updating the Finite Element (FE) model of complex bridge structures employing both static and dynamic experimental measurements. The kriging model is first established to approximate the implicit relationship between structural parameters and responses, serving as a surrogate model for complex FE model when deriving analytical responses. An objective function is later defined based on the residual between analytical response values and experimental measured ones. GAs are finally employed to find the best solution by searching on the whole design space of updating parameters selected based on a sensitivity analysis. To verify the proposed algorithm, Caiyuanba Yangtze River Bridge, a double decked of roadway and light railway bridge with a main span of 420 m is used. Both frequencies and displacements predicted by the updated model are more close to experimental measured ones. The results show that the kriging surrogate model has good accuracy in predicting response and can be used as a surrogate model to reduce computational cost, and GAs provide a higher chance to obtain global best solution.

Short-term Travel-time Prediction on Highway: A Review on Model-based Approach

Abstract: Emerging technologies provide a venue on which on-line traffic controls and management systems can be implemented. For such applications, having access to accurate predictions on travel-times are mandatory for their successful operations. Transportation engineers have developed numerous approaches including model-based approaches. The model-based approaches consider underlying traffic mechanisms and behaviors in developing the prediction procedures and they are logically intuitive unlike datadriven approaches. Because of this explanation power, the model-based approaches have been developed for the on-line control purposes. For departments of transportation (DOTs), it is still a challenge to choose a specific approach that meets their requirements. In efforts to develop a unique guideline for transportation engineers and decision makers when considering for implementing modelbased approaches for highways, this paper reviews model-based travel-time prediction approaches by classifying them into four categories according to the level of details involved in the model: Macroscopic, Mesoscopic, CA-based, and Microscopic. Then each method is evaluated from five main perspectives: Prediction range, Accuracy, Efficiency, Applicability, and Robustness. Finally, this paper concludes with evaluations of model-based approaches in general and discusses them in relation to data-driven approaches along with future research directions.

Integration of Finite Difference Method and Genetic Algorithm to Seismic analysis of Circular Shallow Tunnels (Case Study: Tabriz Urban Railway Tunnels)

Abstract: In this study, it is aimed to investigate the behavior of the concrete lining of circular shallow tunnels in sedimentary urban areas under seismic loads using integration of numerical and metaheuristic techniques. The Tabriz Urban Railway (TUR) Tunnel is used as a case study in this investigation. The seismic and geotechnical characteristics of the area were studied, and seismic analysis was carried out using a finite difference code (i.e., FLAC2D) and genetic algorithm. In the first step, final induced loads on lining due to Design Base Level (DBL), Maximum Credible Level (MCL) and static loads were determined using FLAC2D software. Then, eight parts of lining were classified using genetic algorithm based on axial force, bending moment and shear force for two types of earthquake loads. The results of classification were verified by the safety factors of the studied parts of the lining. By comparing these results, it can be concluded that the genetic algorithm can be reliably used to classify and evaluate the safety of lining based on static and dynamic loads.

Cost Overrun Factor Analysis for Hospital Projects in Vietnam

Abstract: Many governments in the world are now under heavy pressure for improving services of health care whilst always attempt to use their scarce resources efficiently. Hospital buildings are basically one of the important elements of health care systems. This paper aims to determine factors causing cost overrun in Vietnamese hospital projects. Survey questionnaire and Exploratory Factor Analysis (EFA) are two main methods. The finding indicates that main groups causing cost overrun in hospital projects are: additional work, material cost, and delays. Based on groups of cost overrun, twenty factors were also discovered as well as ranked. In addition, solutions were suggested to solve the cost overrun issue, and then comparisons with previous studies were presented. Results of the study could be applied to monitor policies for the management of hospital projects, and support practitioners in attaining the success of hospital projects.

Collaborative innovation in construction project: A social network perspective

Abstract: Successful innovation requires effective cooperation and working relationships among different parties within construction projects. In order to promote construction innovation performance, it is important to shed light on the internal mechanism of innovation through investigating collaborative relationships from a network perspective. In this case, the formation of collaborative relationship can be viewed as a potential generator of innovation processes, and relationship network indicates information exchanges among organizations. This article investigates the collaborative relationship network in a commercial complex by using social network method and in-depth quantitative data analysis. Structural Equation Modeling(SEM) is usually used to analyze the impacts of collaborative relationship on innovation performance in construction projects. There are more and more stakeholders in construction projects, and organization relationship presents a significant network trend. Social network method is widely applied in innovative research. Combined with quantitative data, it is able to quantify and visual the interaction relations of innovation stakeholder. The analytical results will be more objective and reliable. Social network analysis can describe and analyzed collaborative relationship combining qualitative and quantitative method. The results illustrate the relatively dense collaborative relationship networks and highlight the roles that the key members played in the innovation process. The decomposition of collaborative relationship with network analysis contributes to a better understanding of innovation process in construction projects. In particular, key nodes which influence construction innovation through collaborative relationships are revealed and analyzed.

Undrained Lateral Capacity of Rectangular Piles under a General Loading Direction and Full Flow Mechanism

Abstract: New upper and lower bound solutions of undrained lateral capacity of rectangular piles under a general loading direction and full flow mechanism were investigated by using finite element limit analysis with plane strain condition. The true collapse loads of this problem were generally bracketed by computed upper and lower bound solutions to within 3%. Results were summarized in the form of three dimensionless variables, including soil–pile adhesion factor, pile aspect ratio, and lateral loading direction. Predicted failure mechanisms of laterally loaded rectangular piles associated with these parameters were examined and discussed. Approximate equations of failure envelopes for rectangular piles under a general loading direction were proposed for a convenient and accurate prediction of their undrained lateral capacity in practice.

Adsorption of crystal violet from wastewater by modified Bambusa tulda

Abstract: In the present study sodium carbonate modified Bambusa tulda was utilised for the removal of crystal violet dye from aqueous solution. The functional group characterization and the surface morphology was done by Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM). It confirms the hydroxyl groups and carboxyl group present on the surface of modified Bambusa tulda. The optimum condition for the removal of crystal violet was taken place at pH 7, 200 rpm, dose at 10gm/l, initial concentration 50 mg/l, at equilibrium time 60 minutes and 298 K temperature with maximum adsorption capacity of 20.84 mg/gm. The adsorption of crystal violet by modified Bambusa tulda best fits in Langmuir isotherm model with R2 value 0.924 and Pseudo 2nd order rate equation model with R2 value of 0.999. Other parameters like isosteric heat analysis, thermodynamics profile and activation energy were investigated. Thus, modified Bambusa tulda can be an efficient and economically used as an alternative for activated carbon for the removal of crystal violet from waste water.

Principal Stress Rotation under Bidirectional Simple Shear Loadings

Abstract: Previous researches have indicated the non-coaxiality of sand in unidirectional simple shear tests, in which the direction of the principal axes of stresses does not coincide with the corresponding principal axes of strain rate tensors. Due to the limitation of apparatus that most of testing facilities can only add shear stress in one direction, the influence of stress history on the noncoaxiality of sand is not fully considered in previous tests. In this study, the effect of stress history on the non-coaxiality of sand is systematically studied by using the first commercially available Variable Direction Dynamic Cyclic Simple Shear system (VDDCSS). Samples of Leighton Buzzard sand (Fraction B) are first consolidated under a vertical confining stress and consolidation shear stress, and then sheared by a drained monotonic shear stress. Angle (θ) between the consolidation shear stress and the drained monotonic shear stress is varied from 0° to 180°, with an interval of 30°. The change of principal axes of stresses is predicted by well-established equations, and the principal axe of strain rate is calculated using recorded data. Results show that the level of non-coaxiality is increased by the increasing θ, especially at the initial stage of drained shearing.

Wavelet based macrotexture analysis for pavement friction prediction

Abstract: Pavement friction and texture characteristics are important aspects of road safety. Despite extensive studies conducted in the past decades, knowledge gaps still remain in understanding the relationship between pavement macrotexture and surface skid resistance. This paper implements discrete wavelet transform to decompose pavement surface macrotexture profile data into multi-scale characteristics and investigate their suitability for pavement friction prediction. Pavement macrotexture and friction data were both collected within the wheel-path from six High Friction Surface Treatment sites in Oklahoma using a high-speed profiler and a Grip Tester. The collected macrotexture profiles are decomposed into multiple wavelengths, and the total and relative energy components are calculated as indicators to represent macrotexture characteristics at various wavelengths. Correlation analysis is performed to examine the contribution of the energy indicators on pavement friction. The macrotexture energy within wavelengths from 0.97 mm to 3.86 mm contributes positively to pavement friction while that within wavelengths from 15.44 mm to 61.77 mm shows negative impacts. Subsequently, pavement friction prediction model is developed using multivariate linear regressive analysis incorporating the macrotexture energy indicators. Comparisons between predicted and monitored friction data demonstrates the robustness of the proposed friction prediction model.

Model for Predicting Resilient Modulus of Unsaturated Subgrade Soils in South China

Abstract: Subgrade soils are often unsaturated and the resilient modulus (MR) of subgrade soils is usually subjected to the climate environment and traffic loading in the field. Therefore, the Matric Suction (MS) and traffic loading are considered to be two important parameters associated to the MR prediction model. To verify the MR prediction model, the MS of the typical subgrade soil were determined through the pressure plate test. In this study, the soil-water characteristic curves were also described using the Fredlund & Xing’s model. Then, the dynamic MR of the typical subgrade soil under various stresses and water contents was measured. After that, a new prediction model was proposed with the model variables including the minimum bulk stress, octahedral sheer stress and matric suction, and the validity of the new model was verified by previous research results. Finally, the correlations between the physical properties of subgrade soils including the percentage passing through the No. 200 sieve (0.075 mm), plasticity index, liquid limit, dry density and the regression coefficients of the new model were established. The results show that the new model can be used to predict the MR well, and it effectively solves the problem that the bulk stress is equal with a different combinations of the confining pressure and deviator stress. At the same time, the MR can be predicted much more easily with physical parameters of subgrade soils rather than conducting triaxial tests.

Risk Assessment Model for Water and Mud Inrush in Deep and Long Tunnels Based on Normal Grey Cloud Clustering Method

Abstract: In terms of the frequent occurrence and much trouble in governance of the disaster caused by water and mud inrush in deep and long tunnels, the risk assessment model based on normal grey cloud clustering method was proposed. Taking the Jigongling Tunnel of Fanba Expressway as an example, firstly the evaluation target was divided into 8 clustering indices and 4 grey categories according to the grey clustering method. In order to avoid the defects that the traditional whitenization weight functions could not give a good description of system’s randomness and ambiguity, the cloud model was introduced to improve it. Then the whitenization weight values were discretized by using the one-dimensional forward cloud generator to simulate the uncertainties in engineering, and the normal grey cloud whitenization weight functions were established. Afterwards, combined with the engineering data of Jigongling Tunnel collected on site, the clustering weight of each clustering index was analyzed under specific engineering and the clustering coefficient of the target was determined. Lastly the risk of water and mud inrush in Jigongling Tunnel was evaluated using the model. The results, which showed that the risk of water and mud inrush in target D1, D2 and D3 was respectively medium, extremely high and high, were compared with the excavation data. The two coincided with each other well which indicated that the model had a certain engineering value and could provide reference for related engineering.

Evaluation of Rock Vibration Generated in Blasting Excavation of Deep-buried Tunnels

Abstract: The experimental tunnels of the China Jinping Underground Laboratory are constructed in a maximum overburden depth of 2375 m and subjected to extremely high in situ stress more than 50 MPa. When these deep-buried tunnels are excavated with the method of drill and blast, the surfaces created by blasting are generated almost instantaneously, and thus the initial stress on these surfaces is also suddenly released. This transient release of in situ stress causes elastic waves to propagate in rock masses and may have an important effect on the subsequent rock vibration. In this study, a three-dimensional FEM modeling in combination with site investigation is conducted to research the Peak Particle Velocity (PPV) attenuation and frequency characteristics for the rock vibration induced by transient stress release and its combined actions with blast loading. The results indicate that the transient release of the high stress generates considerable vibration velocity that is comparable to that of blast loading. It is not a negligible excitation for the rock vibration generated in blasting excavation of deep-buried tunnels. Furthermore, the vibration induced by transient stress release has much lower frequency than that caused by blast loading. This causes the unloading vibration to decay more slowly and become the major vibration component at far distances. Also, the effect of transient stress release is found to enhance intensity of the total vibration and furthermore cause an increase in its low-frequency content. On the basis of this, the allowable charge amount per delay and the minimum safety distance are finally discussed with a special emphasis on the contributions of the transient stress release to the total vibration.

On The Stiffness Prediction of GFRP Pipes Subjected to Transverse Loading

Abstract: The main objective of this study is to predict the stiffness of GFRP pipes subjected to compressive transverse loading. An experimental study is performed to measure the stiffness of a composite pipe with a core layer of sand/resin composites. Then, a simple analytical modeling constructed on the basis of solid mechanics is used to estimate the stiffness of the investigated pipe as the back-of-envelope technique widely used by industrial sectors. The simulation of stiffness test is conducted using finite element modeling wherein both large deformation and inelastic behavior of material is taken into account as the sources of nonlinearity. The results reveal that a very good estimation with high level of accuracy can be reached by proper selection of the element and performing nonlinear analysis.

Adsorptive Treatment of Landfill Leachate using Activated Carbon Modified with Three Different Methods

Abstract: Activated Carbon (AC) is an adsorbent having high surface area which makes the process of removing heavy metals from wastewater (such as landfill leachate) very effective. This study explored the utilization of three methods of modification of AC produced from coconut shell by treating it with nitric acid (HNO3), potassium permanganate (KMnO4) and heating at 600°C to improve the adsorption capacity. The AC can remove multi-pollutants in the filtration process which was used to treat landfill leachate. The water quality parameters such as pH, TSS, Ammonia-Nitrogen and a few heavy metals were considered in the present study. Results showed that the removal of these parameters was proportional with the increase of contact time and the bed depth of AC. The isotherm analysis of the adsorption of modified AC showed the best Removal Efficiency (RE) can be achieved when AC treated with KMnO4 for NH3-N, zinc, TSS and sulphide. The morphology of the AC was studied through Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX) pattern analysis and Fourier Transform Infrared (FTIR) analysis. It was found that various types of oxygen functional groups were introduced onto the surface of coconut shell derived AC through oxidation using HNO3. FTIR was used to characterize the surface oxygen functional groups. The surface functional groups such as N-H and C-H stretching played a significant role in heavy metals adsorption. Hence, it can be concluded that the hybrid technique by using electrolysis process with AC adsorption be an effective way to remove the suspended solids and heavy metals from landfill leachate and thus able to reduce environmental pollution.

Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas

Abstract: The Ministry of Employment and Labor releases its annual report on the present conditions of industrial disasters by aggregating and summarizing negligent accidents that occur at construction sites. Industry-specific accident and fatality rates, and disaster classification and statistics are aggregated in this report, but its effectiveness is low. This is due to the fact that it does not sufficiently present the direct causes of accidents or related information on their causal relation. However, this study utilizes a big-data method that has recently gained significant attention throughout all industrial and academic areas to collect Internet articles on fire-accidents that have occurred at construction sites over the last decade. In addition, principal component analysis was conducted to deduce season-specific factors according to time, location, inducer, and accident pattern. Based on this analysis, as for common factors, direct spark and oil mist were deduced. As work-related factors, negligent supervision and violations of the safety regulations were shown to cause fire-accidents, illustrating the man-made nature of such accidents. It was also found that secondary accidents such as collapses, burials, explosions, and suffocation have occurred when fires have broken out. The big-data analysis method utilized in this study is considered to be very effective and can be successfully utilized in the future for deducing high volumes of text data.

Applications of unmanned aerial vehicles in fluvial remote sensing: An overview of recent achievements

Abstract: Previously, understanding of the fluvial process from the ecological, morphological, and hydrodynamic perspectives has largely relied on the limited scale of in-situ field observation or the sparse spatial and temporal scale of satellite-based remote sensing. However, with the recent advent of unmanned aerial vehicles (UAVs) and concurrent advances in sensor technology, measurement campaign has been revolutionized and the view of rivers has fundamentally changed from the local scale to the holistic scale; the perspective has shifted from a static to a dynamic one. UAVs can provide a fine spatial and temporal resolution of measurements with a relatively low cost, which, as compared to conventional satellite or pilot-controlled airborne systems, can be more suitable for the analysis of fluvial processes in narrow rivers and small lakes. In this paper, we comprehensively review and document various crucial achievements driven by UAVs-based remote sensing in fluvial environments, among a variety of other relevant applications. Specifically, the paper highlights the UAV-based fluvial remote sensing in terms of riparian vegetation, hazardous aquatic algae blooms, submerged morphology, water-surface slope, sediment, flow velocity, and disasters, including flood inundation mapping.

Cyclic and Post-Cyclic Shear Behaviour of Interface between Geogrid and EPS Beads-Sand Backfill

Abstract: Lightweight sand–EPS beads composite is a new artificial geo-material, which has been recently found applications in geotechnical engineering projects. A series of laboratory tests were carried out using a large-scale cyclic direct shear test apparatus to evaluate the monotonic, cyclic and post-cyclic behaviour of an interface between EPS-sand mixtures and a Polyfelt geogrid. EPS were added to sand at 0%, 0.5%, 1%, and 2% by weight. Tests were conducted under three different vertical stresses (30 kPa, 60 kPa and 90 kPa). The influences of cyclic shear semi-amplitude, number of cycles and normal stress on interface properties are investigated. The test results revealed that for a given strain level, interface shear stiffness decreases and damping ratio was shown to increase with increasing EPS content. Hardening behaviour was observed with the number of cycles under different normal stress levels and EPS contents. The EPS-sand-geogrid specimens did not develop clear peak shear stress at monotonic and post-cyclic direct shear tests. The EPS-sand-geogrid mixtures represent an overall contraction behaviour in monotonic, cyclic and post-cyclic stages. The apparent adhesion of interface was shown to increase and the friction angle of interface to decrease with EPS content.

Optimal reservoir rule curves under climatic and land use changes for Lampao Dam using Genetic Algorithm

Abstract: The uncertainties of climate and land use changes have directly impacted the inflows and water resource management in reservoirs. The optimal reservoir rule curve is a tool for the mitigation of droughts and floods, which are situations that occur often. This study applied the Genetic Algorithm (GA) to connect with a reservoir simulation model to search optimal reservoir rule curves during the period 2014-2064 for Lampao Reservoir located in the northeast of Thailand. It considered the impact of climate change with the PRECIS model under two emission scenarios: A2 and B2, and created future land use maps using the CA Markov model, including an assessment of the future inflow into the reservoir using the hydrologic model SWAT in the Upper-Lampao Basin, which is the headwater area of the reservoir. The results showed that the new rule curves were improved by the GA connected simulation model and can mitigate the frequency of water shortage situations and the releases of excess water during inflow changes in the future, including a situation where the water demand increased due to the expansion of irrigation areas.