Bridge Station Research Articles

Evaluating responsive gateways amid future uncertainty at the London Bridge station (UK)

The high concentration of people in train stations during rush hour poses a significant risk to passenger comfort and safety. Recent technological advancements in real-time crowd counting and pedestrian microsimulations have made it feasible to create responsive gateways for managing passenger flow to prevent overcrowding situations. This paper proposes a simulation-based cost–benefit analysis, utilising the real options methodology commonly employed to assess flexible infrastructure, to estimate the costs and benefits associated with implementing responsive gateways at the London Bridge station, UK, while considering uncertainties in passenger flow and the interests of various stakeholders. Results from the demonstrative example indicate that responsive gateways offer a better service than static ones mainly due to the expected reduction in the risks related to safety, travel time and comfort, which compensates for a higher cost of installation and maintenance. Further research is envisioned to enhance the modelling of passenger flow uncertainties and expand the objective function to encompass additional externalities. The simulation-based cost–benefit analysis proposed in this paper can be extended to a broader range of intelligent transportation systems, considering uncertainties and their diverse impacts on society. Thus, this paper presents a tool that contributes to a more transparent and objective investment decision making process, particularly concerning the development of smart cities.

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data

Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.

Analysis of the Queuing System and the Access Bridge of the Kuningan LRT Station

LRT or Light Rail Transit is an alternative to mass transportation from the outskirts to the city center, helping to cut travel time and is integrated with public transportation and activity center locations. LRT effectiveness needs to be supported by LRT stations, the factors included are passenger queues and access bridge. This research delves into an analysis of the queuing system and access bridge at Kuningan LRT station, Jakarta, focusing on the expected waiting time in the system (card tapping time) and the queue at the north and south entrances. The research methodology involved a combination of observation and analytical methods, including data collection on the dimension of the access bridge and LRT station and the use of Queuing theory with multiple single servers' models for data analysis. The research findings reveal that during peak hours, with an estimated daily influx of 10,990 passengers arriving at Kuningan LRT and 10,096 departing from it, the average number of people in the queue is less than one person, with no prolonged queues. The analysis of data presented in this study reveals that the design and construction of the Kuningan LRT access bridge align closely with the pertinent development regulations. The study contributes to the knowledge of queuing systems and provides insights into the number of people estimated to be queuing at the Kuningan LRT station and to forecast how many queues may form during peak hours.

Uncertainty assessment for three-dimensional hydrodynamic and fecal coliform modeling in the Danshuei River estuarine system: The influence of first-order parametric decay reaction

Modeling fecal contamination in water bodies is of importance for microbiological risk assessment and management. This study investigated the transport of fecal coliform (e.g., up to 2.1 × 106 CFU/100 ml at the Zhongshan Bridge due to the main point source from the Xinhai Bridge) in the Danshuei River estuarine system, Taiwan with the main focus on assessing model uncertainty due to three relevant parameters for the microbial decay process. First, a 3D hydrodynamic-fecal coliform model (i.e., SCHISM-FC) was developed and rigorously validated against the available data of water level, velocity, salinity, suspended sediment and fecal coliform measured in 2019. Subsequently, the variation ranges of decay reaction parameters were considered from several previous studies and properly determined using the Monte Carlo simulations. Our analysis showed that the constant ratio of solar radiation (α) as well as the settling velocity (vs) had the normally-distributed variations while the attachment fraction of fecal coliform bacteria (Fp) was best fitted by the Weibull distribution. The modeled fecal coliform concentrations near the upstream (or downstream) stations were less sensitive to those parameter variations (see the smallest width of confidence interval about 1660 CFU/100 ml at the Zhongzheng Bridge station) due to the dominant effects of inflow discharge (or tides). On the other hand, for the middle parts of Danshuei River where complicated hydrodynamic circulation and decay reaction occurred, the variations of parameters led to much larger uncertainty in modeled fecal coliform concentration (see a wider confidence interval about 117,000 CFU/100 ml at the Bailing Bridge station). Overall, more detailed information revealed in this study would be helpful while the environmental authority needs to develop a proper strategy for water quality assessment and management. Owing to the uncertain decay parameters, for instance, the modeled fecal coliform impacts at Bailing Bridge over the study period showed a 25 % difference between the lowest and highest concentrations at several moments. For the detection of pollution occurrence, the highest to lowest probabilities for a required fecal coliform concentration (e.g., 260,000 CFU/100 ml over the environmental regulation) at Bailing Bridge was possibly greater than three.

Application of novel binary optimized machine learning models for monthly streamflow prediction

Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.

Development of Water Level Prediction Improvement Method Using Multivariate Time Series Data by GRU Model

The methods for improving the accuracy of water level prediction were proposed in this study by selecting the Gated Recurrent Unit (GRU) model, which is effective for multivariate learning at the Paldang Bridge station in Han River, South Korea, where the water level fluctuates seasonally. The hydrological data (i.e., water level and flow rate) for Paldang Bridge station were entered into the GRU model; the data were provided by the Water Resources Management Information System (WAMIS), and the meteorological data for Seoul Meteorological Observatory and Yangpyeong Meteorological Observatory were provided through the Korea Meteorological Administration. Correlation analysis was used to select the training data for hydrological and meteorological data. Important input data affecting the daily water level (DWL) were daily flow rate (DFR), daily vapor pressure (DVP), daily dew point temperature (DDPT), and 1 h max precipitation (1HP), and were used as the multivariate learning data for water level prediction. However, the DWL prediction accuracy did not improve even if the meteorological data from a single meteorological observatory far from the DWL prediction point were used as the multivariate learning data. Therefore, in this study, methods for improving the predictive accuracy of DWL through multivariate learning that effectively utilize meteorological data from each meteorological observatory were presented. First, it was a method of arithmetically averaging meteorological data for two meteorological observatories and using it as the multivariate learning data for the GRU model. Second, a method was proposed to use the meteorological data of the two meteorological observatories as multivariate learning data by weighted averaging the distances from each meteorological observatory to the water level prediction point. Therefore, in this study, improved water level prediction results were obtained even if data with some correlation between meteorological data provided by two meteorological observatories located far from the water level prediction point were used.

Environmental Flow Assessment of Gorai River in Bangladesh: A comparative analysis of different hydrological methods

The purpose of the study is to assess the Environmental Flow Requirement (EFR) of Gorai River in Bangladesh and to evaluate the change in flow characteristics in recent time compared to past. Daily discharge data of Gorai railway bridge station were collected from Bangladesh Water Development Board and analyzed for two periods. Mean Annual Flow (MAF) in G2 period (2000–20166) is found about 13% lower than G1 period (1984–1999). The Mean Monthly Flow (MMF) in low flow season is increased by 99%, and that of high flow is decreased by 20% in G2 period compared to G1. In this study, EFR was determined considering various methods including Tennant, Tessmann, Variable Monthly Flow, Modified Constant Yield, FDCA Q50-Q90, FDCA Q50-Q75 and Smakhtin Method. The average EFR for low, high and intermediate flows were found as 89, 915 and 273 m3/s, which is 9, 61 and 27% of MAF and 96, 23 and 61% of mean seasonal flow, respectively. The overall annual EFR for the river is found as 295 m3/s or 29% of MAF. It is observed that when the EFRs are expressed as percent of mean seasonal flow, the Low-Flow Requirements (LFR) were found higher than High-Flow Requirements (HFR). However, when the EFRs are expressed as percent of MAF, the LFR is lower than HFR. Among all the EFRs predicted by 8 methods, Smakhtin predicts the smallest HFR (6% of MAF) and FDCA Q50-Q90 have the lowest LFR value (1.2% of MAF). The Tennant method is not found to be capable to capture the temporal change of MMF of different seasons. The Average Annual EFR was found to be reduced by 14% in latter period. A deficient flow situation was observed from December to May. The findings can be used for future reference in management of flows in Gorai river.

Microbiological properties of water and sediment of New Calabar River, Port Harcourt, Nigeria

Microbiological characteristics of water and sediment of New Calabar, River, Port Harcourt, Nigeria were investigated. Water and sediment samples were collected from three stations {Aluu, ARAC (Station 1; Choba Bridge (Station 2) and Rumuokparali (Station 3)} on a monthly basis between February and April, 2020 and analyzed using standard methods. The results showed that Total Heterotrophic Bacteria in water (THBw) ranged between 2.20 x 102 and 2.6x104 cfu/ml with the overall mean value of 7,313.33 + 1039.09 cfu/ml while that of sediment (THBs) ranged between 3.1 x 103 and 3.9 x 104 cfu/g with the overall mean value of 42,566.67 + 71199cfu /g. The THB counts were higher in sediment than in water with Station 2 significantly different from other stations. Total Coliform Bacteria (TCB) in water ranged between 1.1 x 103 cfu/ml and 2.9 x 103 cfu/ml with the mean value of 1988.89±532.55cfu/ml while that of sediment ranged between 1.3 x 103 cfu/g and 2.5 x 104 cfu/g with the mean value of 9422.22 ±9595.41cfu/g. TCB counts in water showed no significant difference across the stations at p<0.05 but Station 2 statistically differed from Stations 1 and 3 in the sediment. Morphological and biochemical tests showed that 8 bacteria species were isolated from sediment and and 9 specimens were recovered from water. Some isolated specimens were: Bacillus spp, Staphylococcus. sp, Bacillus cereus and Bacillus subtilis while Escherichia coli was the only bacterium found in water but absent in sediment. Two species: Staphylococcus spp and Bacillus subtilis dominated the study area. Fungal counts in water ranged between 3.0 x102 and 2.6 x 103 cfu/ml with the overall mean value of 1911.11±67.51cfu/ml while that of sediment ranged between 1.7 x101 and 3x102 with the overall mean value of 173.00±78.52 cfu/g. Fungi counts showed significant difference (p < 0.05) across the stations in water and sediment. Macroscopic and microscopic characterization of fungi showed that 6 fungi species were recovered in water and sediment. These included Aspergillus niger, Rhizopus oligospora and Pennicillium species. The species Fusarium solani and Aspergillus flavor were found in Station 1 but absent in Stations 2 and 3. It could be observed that microbial counts in both water and sediment exceeded the 1.0x102 cfu/ml and 0 cfu/ml recommended levels by WHO and EPA respectively. Thus, the study area may be under threat and the water may be unsafe for human consumption.
 Keywords: Microbiological Properties, Sediment, Water, New Calabar River, Port Harcourt

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (R2=0.7480–0.8318; NSE=0.7524–0.7965; MRPE=0.0807–0.0895) were obtained than those of water-level prediction results by univariate training.

Comparative analysis of long short-term memory and storage function model for flood water level forecasting of Bokha stream in NamHan River, Korea

In this study, the applicability of machine learning models was investigated for real-time flood forecasting of a small river basin with a short time of concentration and the modes were compared with the storage function model (SFM), a rainfall-runoff model. Bokha stream basin located in the NamHan river, Korea was selected as the study area. Flood water level forecasting was performed for Heungcheon bridge station which is located in the downstream of Bokha stream using hydrological data observed at Bokha bridge station located in the upstream of the stream. For each of the upstream and downstream basins, Rainfall, water level, and discharge data from 2005 to 2020 were collected at two stations and especially the collected rainfall data were classified into 53 rainfall events using Interevent Time Definition (IETD) analysis of rainfall data. In addition, flood water level forecasting at downstream point was performed using machine learning models such as GB (Gradient Boosting), SVM (Support Vector Model), and LSTM (Long Short-Term Memory). Also the SFM which is a rainfall-runoff model was used for the forecasting. For the application of machine learning models, 33 rainfall events were used for learning and 23 rainfall events were used for evaluation. With the SFM, the flood discharge was forecasted first and then the flood water level was forecasted through the rating curve. The flood water level forecasted by each model was compared with the observed flood water level and predictive power for each model was evaluated by calculating NRMSE (Normalized Root Mean Squared Error). The NRMSEs for the models were ranged from 0.18 to 0.27, and the predictive power was good in the order of the LSTM model at 0.18 followed by the SFM at 0.21. Therefore, the LSTM model showed the best predictive power and was selected as the optimal model for real-time flood water level forecasting in this study. However, the SFM is currently employed in Korea for flood forecasting and warning, and the model well incorporates the basin characteristics, showing relatively good predictive power. Based on the models presented in this study, an optimal model suitable for real-time flood water level forecasting can be selected for flood forecasting and warning points of a small river basin, and it is expected that the forecasting results can be used as base data for decision making.

AN ASSESSMENT OF A RIVER HYDROLOGICAL REGIME ALTERATION: A CASE STUDY OF SUNGAI DUNGUN

Hydro-morphology of a river can be assessed using four main methods namely morphological assessment, physical habitat assessment, riparian habitat assessment and hydrological regime alteration assessment. This study focused on the fourth method where changes in flow rate are the criteria of river regime alteration. The Indicator of Hydrologic Alteration (IHA 7.0) was employed to study the pre- and post-impact of a major flood event in 2014 at Sungai Dungun as well as the other anthropogenic stress such as sand mining activities that occurred at Sungai Dungun. The daily flowrate from the monitoring site at Jerangau bridge (Station Number: 4832441) was obtained from DID (Department of Irrigation and Drainage) to assess the pre-impact flowrate (2002 – 2014) and post-impact flowrate (2015-2019). It was found that the flow duration curve decreased at the post-impact significantly compared to the pre-impact duration. The median flows dropped almost half between pre-impact and post-impact analysis. A few indicators were identified as the high range of variability approach (RVA) that defines the extent of alteration between pre- and post-analysis. The positive RVA of fall rate, high pulse duration, low pulse duration signifies the increase of frequency from pre-impact to the post-impact while negative RVA of 90-1-day minimum flows, 30-7 days maximum flows December, July and May median flow indicate the frequency of values in the category has decreased between pre- and post-analysis.

Seasonal influence of water quality on the assemblage and diversity of phytoplankton along Dubai Creek, Arabian Gulf

The research aims to find out the seasonal variation in phytoplankton diversity, composition and their abundance in response to various environment parameters. Seasonal variation in the phytoplankton community associated with various biological and water quality parameters (Temperature, Salinity, Dissolved Oxygen (DO), Nitrate (NO3-N), Phosphate (PO4-P), Chlorophyll-a, Phaeophytin and Carotenoids) were studied along 10 sampling stations of the Dubai Creek during January–December 2018. One-way ANOVA (p< 0.005) and correlation coefficient between the phytoplankton abundance and the physico-chemical parameters revealed positive trends of phytoplankton composition. The phytoplankton species diversity varied from 55 (Sanctuary) to 95 (Creek mouth). A total of 110 species of phytoplankton were recorded in Dubai Creek, comprising of 76, 29, 3, 1 and 1 species of Bacillariophyceae, Dinophyceae, Cyanophyceae, Euglenophyceae and Ciliophora respectively. The annual mean variation in phytoplankton population were recorded between 744 (Hyatt Regency) to 31906 cells × 103 L−1 (STP outfall). Due to the morphology, hydrodynamic regime and polluted nature in the upper Creek presented an unique phenomenon where the phytoplankton species and density divided Dubai Creek into two (2) sections. The first section (downstream of the Dubai Creek) include the stretch from the Creek mouth to the Floating bridge stations with low population and high species diversity, whereas the other section (upstream of the Dubai Creek) cover stations from Al Gharhoud bridge up to the Sanctuary with dense population and low species diversity. The diversity index showed highest along downstream of Dubai Creek. A ‘k’ dominance plots for phytoplankton abundance between samples showed lower and higher steep curves along downstream (Creek mouth, Abra and Hyatt Regency) and upstream regions (Al Jaddaf, Sanctuary and STP outfall) of Dubai Creek. This could probably de due to the polluted and unpolluted conditions of the Creek. Bray–Curtis cluster analysis showed strong similarity (>60%) among upstream stations of Dubai Creek associated with polluted conditions, whereas an overlap of Creek mouth, Abra and Hyatt Regency on remaining stations showed unpolluted and healthy conditions of the phytoplankton in downstream stations. Canonical Correspondence Analysis revealed that Bacillariophyceae have a positive strong relationship with different environment variables between stations Abra, Hyatt Regency and Creek mouth, indicating influence of high flushing of the coastal waters on phytoplankton species and diversity. The study presents an outline of the seasonal dynamic relationship between phytoplankton structure and environmental conditions in the Dubai Creek.

Flood Risk Assessment in Urban Areas of Southern Taiwan

A flood risk assessment of urban areas in Kaohsiung city along the Dianbao River was performed based on flood hazards and social vulnerability. In terms of hazard analysis, a rainfall-runoff model (HEC-HMS) was adopted to simulate discharges in the watershed, and the simulated discharges were utilized as inputs for the inundation model (FLO-2D). Comparisons between the observed and simulated discharges at the Wulilin Bridge flow station during Typhoon Kongrey (2013) and Typhoon Megi (2016) were used for the HEC-HMS model calibration and validation, respectively. The observed water levels at the Changrun Bridge station during Typhoon Kongrey and Typhoon Megi were utilized for the FLO-2D model calibration and validation, respectively. The results indicated that the simulated discharges and water levels reasonably reproduced the observations. The validated model was then applied to predict the inundation depths and extents under 50-, 100-, and 200-year rainfall return periods to form hazard maps. For social vulnerability, the fuzzy Delphi method and the analytic hierarchy process were employed to select the main factors affecting social vulnerability and to yield the weight of each social vulnerability factor. Subsequently, a social vulnerability map was built. A risk map was developed that compiled both flood hazards and social vulnerability levels. Based on the risk map, flood mitigation strategies with structural and nonstructural measures were proposed for consideration by decision-makers.

Thameslink programme, UK: design and construction of London Bridge station

Completed in 2019, the Thameslink Programme was a £7 billion scheme sponsored by the UK government to transform north–south rail travel across London. One of the most challenging elements was the redevelopment of London Bridge station. Following an earlier paper on the collaborative partnership between the client and delivery team, this paper focuses on the engineering challenges. These included removal of 140 Victorian arches to create a large new concourse beneath the platforms, design of bridges to cross the concourse that could be constructed in island sites between live tracks, and fabrication of modular platforms and canopies with hundreds of individual units to follow the curve of the tracks.

Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea

Reliable runoff series is sine qua non for flood or drought analysis as well as for water resources management and planning. Since observed hydrological measurement such as runoff can sometimes show abnormalities, data quality control is necessary. Generally, the data of adjacent hydrological stations are used. However, difficulties are frequently encountered when runoff series of the adjacent stations have different flow characteristics. For instance, when the correlation between the up- and downstream locations in which the stations are located is used as the main criterion for quality control, difficulties can occur. Therefore, this study aims to suggest a method to reconstruct an abnormal daily runoff series in the Nakdong River, Korea. The variational mode decomposition (VMD) technique is applied to the runoff series of the three target stations: Goryeong County (Goryeong bridge) and Hapcheon County (Yulji bridge and Jeogpo bridge). These runoff series are also divided into several intrinsic mode functions (IMFs) that are governed by basin runoff and disturbed flow caused by the hydraulic structure. The decomposition results based on VMD show that the runoff components in a particular station that is influenced by hydraulic structures could be reconstructed using adjacent stations, but the residual mode could not. The runoff reconstruction model using an artificial neural network (ANN), the two “divided” modes, and the residual component is established and applied to the runoff series for the target station (Yulji bridge in Hapcheon County). The reconstructed series from the model show relatively good results, with R2 = 0.92 and RMSE = 99.3 in the validation year (2019). Abnormal runoff series for 2012 to 2013 at the Yulji bridge station in Hapcheon County are also reconstructed. Using the suggested method, a well-matched result with the observations for the period from 2014 onwards is produced and a reconstructed abnormal series is obtained.

A study on the evaluation of the water quality status for the Büyük Menderes River, Turkey

This study was carried out to assess the water quality of the Buyuk Menderes River, as per the criteria of water quality index (WQI) by analyzing 15 physical and chemical parameters in 8 observation stations. The WQI method is considered as one of the most efficient methods of measuring the quality of water. Average WQI values were calculated between 37.27 and 85.96 based on the stations examined, and average values based on months were calculated as 56.88 and 71.38. The highest WQI values at the stations were recorded during April, June, and October. The lowest WQI value was found in the Yenice station and the highest value was recorded at the Saraykoy bridge station. WQI values have varied over a wide range across the entire river. According to WQI scores, statu of river water quality varies between “good” and “very poor”. The water quality statu at the stations located at the upstream and downstream of the river route is “good”, but at the stations located in the central part of the river, can be classified as “poor” and “very poor”. This is mostly due to the aggregation of urban and rural settlements and all commercial activities, which are clustered in the middle part of the river route. Therefore, to prevent pollution of the river and to maintain the water quality, the wastewater originating from domestic sources as well as from industrial activities should be processed and treated before its discharge into the river, and the fertilizers and pesticides utilized in agriculture must be regulated throughout the basin to reduce their exposure to water.

Deformation and Force Analysis of the Pile Foundation System of Deep Foundation Pits in Soil–Rock Combination

This study uses the construction of the Shengli Bridge Station of Qingdao Metro Line 1 as the engineering support for in-depth research of the deformation and mechanical characteristics of the retaining structure of pile-support systems during deep foundation pit excavation under the complex geological conditions of soil–rock combination. This study adopts the finite element software modeling analysis method to simulate the excavation of the foundation pit and the 3D finite element numerical calculation method to calculate the deformation. The modified Mohr–Coulomb model is selected as the constitutive model, and the natural gravity of each layer of soil is considered. The calculated load includes the dead load of structure and soil. The boundary conditions of X, Y, and Z directions at the bottom are fixed constraints, and the top ground is a free boundary. The displacement of soil around the foundation pit and the stress and deformation of retaining piles and crown beams under different working conditions are studied. The deformation law and force mechanism of the supporting structure of the pile-support system are analyzed. The research results reveal the relationship between the synergy of pile-support system and soil deformation, serving as a reference for similar engineering constructions.

AWWA Water Science Author Spotlight

Having recently published an article in AWWA Water Science, Cristina Marcillo answered a few questions from its editor-in-chief Kenneth L. Mercer about the research. Small Towns, Big Challenges: Does Rurality Influence Safe Drinking Water Act Compliance? Cristina E. Marcillo and Leigh-Anne H. Krometis Cristina travels to Guatemala with many bags of sampling equipment. Photo courtesy of Cristina E. Marcillo Cristina samples a stream in Guatemala. © 2020 Richard E. Bishop. Printed with permission. My recent article in AWWA Water Science (AWS) was motived by the goal of understanding distinct violation patterns and subsequent contaminant exposure in urban and rural parts of Virginia. Part of my research goal is to contextualize water system violations to understand how compliance might be influenced by local factors like urban/rural status and how these local factors might be creating different climates of environmental health. One of my broader goals has been to analyze similar data accounting for service area demographics, and my recent publication in AWS was one step toward that goal. Cristina hikes Devil's Marbleyard near Natural Bridge Station, Va. Photo courtesy of Cristina E. Marcillo I am really excited by the research that I do, so it can be hard to strike a balance when research itself is fun. But in my free time, I like to hike along the many beautiful trails in southwest Virginia, read, meditate, volunteer, and visit local breweries with friends. To learn more about Cristina's research, see the abstract on page 21 of the February 2019 issue of Journal AWWA and read the entire article in AWWA Water Science, available online at https://doi.org/10.1002/aws2.1120.

Bivariate flood distribution analysis under parametric copula framework: a case study for Kelantan River basin in Malaysia

Flood is becoming an intensive hydro-climatic issue at the Kelantan River basin in Malaysia. Univariate frequency analysis would be unreliable due to multidimensional behaviour of flood, which often demands multivariate flow exceedance probabilities. The joint distribution analysis of multiple interacting flood characteristics, i.e. flood peak, volume and duration, is very useful for understanding critical hydrologic behaviour at a river basin scale. In this paper, a copula-based methodology is incorporated for multivariate flood frequency analysis for the 50-year annual basis flood characteristics of Kelantan River basin at Guillemard bridge station in Malaysia. Investigation reveals that the Lognormal (2P), Johnson SB-4P and Gamma-3P are selected as marginal distributions for the flood peak flow, volume and duration series. Several bivariate families such as mono-parametric, bi-parametric (i.e. mixed version) and rotated version of Archimedean copulas and also the elliptical copula are introduced to cover a large dependence pattern of flood characteristics. The dependence parameter of bivariate copulas is estimated by the method of moments (MOM) based on the inversion of Kendall’s tau and maximum pseudo-likelihood estimator. To analytically validate and recognize most parsimonious copulas, GOF test and Cramer–von Mises distance statistics (Sn) with the parametric bootstrap method are employed. The Gaussian copula is identified as the most justifiable model for joint modelling of the flood peak–volume and peak–duration combination for MOM-based parameter estimation procedure. Similarly, the Frank copula is selected as the best-fitted structure for modelling peak–duration combination based on MPL estimators, but the MOM estimator recognized Gaussian copula as most suitable for peak–volume pair. Furthermore, the best-fitted copulas are used for obtaining the joint and conditional return periods of the flood characteristics.

Exploring the Relationships between the Topological Characteristics of Subway Networks and Service Disruption Impact

Although numerous studies have considered the topological characteristics and the impact of disruptions in subway systems, their results have not been verified by empirical data. To address this limitation, we used a data set containing 392 detailed records of disruptions to subway services in Beijing from 2011 to 2017. The Spearman rank correlation coefficient analysis results indicate that the delay duration exhibits no significant relationship with the topological characteristics, whereas the reverse is true for the relationship between the number of affected trains and the topological characteristics. The results also demonstrate that subway network expansion will not result in a paradox between convenience and vulnerability from an actual data perspective. Moreover, contrary to previous research results, no significant relationship was found to exist between service interruption impact and the transit and key bridge stations. However, a high degree of clustering, characterized by redundant tracks between neighbours, tends to provide protection against service disruption for stations. In terms of the spatial variation, the influence of the disruption is greater when the station is further from the centre of the line. These results can support sustainable design in subway network planning.