Korea Institute of Civil Engineering and Building Technology

Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

This paper investigates changes in storm runoff resulting from the transformation of previously rural landscapes into peri-urban areas. Two adjacent catchments (∼5 km2) located within the town of Swindon in the United Kingdom were monitored during 2011 and 2012 providing continuous records of rainfall, runoff and actual evaporation. One catchment is highly urbanized and the other is a recently developed peri-urban area containing two distinct areas of drainage: one with mixed natural and storm drainage pathways, the other entirely storm drainage. Comparison of observed storm hydrographs showed that the degree of area serviced by storm drainage was a stronger determinant of storm runoff response than either impervious area or development type and that little distinction in hydrological response exists between urban and peri-urban developments of similar impervious cover when no significant hydraulic alteration is present. Historical levels of urbanization and impervious cover were mapped from the 1960s to the 2010s based on digitized historical topographic maps and were combined with a hydrological model to enable backcasting of the present day storm runoff response to that of the catchments in their earlier states. Results from the peri-urban catchment showed an increase in impervious cover from 11% in the 1960s to 44% in 2010s, and introduction of a large-scale storm drainage system in the early 2000s, was accompanied by a 50% reduction in the Muskingum routing parameter k, reducing the characteristic flood duration by over 50% while increasing peak flow by over 400%. Comparisons with changes in storm runoff response in the more urban area suggest that the relative increase in peak flows and reduction in flood duration and response time of a catchment is greatest at low levels of urbanization and that the introduction of storm water conveyance systems significantly increases the flashiness of storm runoff above that attributed to impervious area alone. This study demonstrates that careful consideration is required when using impervious cover data within hydrological models and when designing flood mitigation measures, particularly in peri-urban areas where a widespread loss in pervious surfaces and alteration of drainage pathways can significantly alter the storm runoff response. Recommendations include utilizing more refined urban land use typologies that can better represent physical alteration of hydrological pathways.

Soil treatment and improvement is commonly performed in the field of geotechnical engineering. Methods and materials to achieve this such as soil stabilization and mixing with cementitious binders have been utilized in engineered soil applications since the beginning of human civilization. Demand for environment-friendly and sustainable alternatives is currently rising. Since cement, the most commonly applied and effective soil treatment material, is responsible for heavy greenhouse gas emissions, alternatives such as geosynthetics, chemical polymers, geopolymers, microbial induction, and biopolymers are being actively studied. This study provides an overall review of the recent applications of biopolymers in geotechnical engineering. Biopolymers are microbially induced polymers that are high-tensile, innocuous, and eco-friendly. Soil–biopolymer interactions and related soil strengthening mechanisms are discussed in the context of recent experimental and microscopic studies. In addition, the economic feasibility of biopolymer implementation in the field is analyzed in comparison to ordinary cement, from environmental perspectives. Findings from this study demonstrate that biopolymers have strong potential to replace cement as a soil treatment material within the context of environment-friendly construction and development. Moreover, continuing research is suggested to ensure performance in terms of practical implementation, reliability, and durability of in situ biopolymer applications for geotechnical engineering purposes.

This study investigated the feasibility of reducing fiber content in ultra-high-performance fiber-reinforced cement composites (UHP-FRCC). For this, three different types of steel fibers were considered, and three different aspect ratios were applied for the case of straight fibers. To quantitatively evaluate the cost effectiveness of reducing the fiber content of UHP-FRCC, cost analysis was also performed. Test results indicated that at low fiber volume fractions (Vf ≤ 1.0%), the twisted fibers provided the highest flexural strength, but they exhibited similar strength and poorer toughness than the straight fibers at a Vf equal to or higher than 1.5%. Smaller flexural strength and toughness were observed in the specimens with hooked fibers than those with straight ones at a Vf equal to or higher than 1.0%. In the case of straight fibers, the one with the highest aspect ratio was more effective in improving the flexural performance than those with lower aspect ratios. The medium-length straight fibers were most efficient at improving the flexural performance of UHP-FRCC at a Vf equal to or higher than 1.5%. The total production costs of commercially available UHP-FRCC could be reduced by as much as 32–35% by replacing short straight fibers with medium-length or long straight fibers.

Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this article, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current methods.

Ultrahigh-performance concrete (UHPC) offers significant potential to address a variety of needs in bridge design, construction, and performance enhancement. Bridge owners have shown willingness to embrace novel solutions that could address specific challenges related to the cost, speed of construction, durability, and service life of their projects. There are hundreds of bridges worldwide that, largely in the past decade, have utilized UHPC. These applications range from minor field-cast closures to precast segments for long-span bridges to kilometer-long bridge deck overlays on a signature structure. The objective of this paper is to promote the application of this class of cementitious material in bridge engineering by presenting the progress that has been made in different regions of the world in the past two decades. Today, UHPC is being widely used in Malaysia to design and construct many bridges of different types and spans as they build out their roadway network. In South Korea, the unique characteristics of UHPC are being utilized to advance the state-of-the-art in long-span bridges. The French were early adopters and pioneers in building a strong foundation for using UHPC in a variety of bridge applications. In Switzerland, UHPC is employed to address major bridge rehabilitation needs. The United States bridge sector has embraced UHPC for a variety of field-cast connections. Current research and development efforts are promoting the use of UHPC in major rehabilitation projects and construction of primary bridge components. The adoption of UHPC solutions into the bridge sector is progressing rapidly because of the unique opportunities provided by the strength and durability of the material. It is expected that additional innovations and refinements of solutions will occur as knowledge of the material proliferates.

The birth of smart materials such as piezoelectric (PZT) transducers has aided in revolutionizing the field of structural health monitoring (SHM) based on non-destructive testing (NDT) methods. While a relatively new NDT method known as the electromechanical (EMI) technique has been investigated for more than two decades, there are still various problems that must be solved before it is applied to real structures. The technique, which has a significant potential to contribute to the creation of one of the most effective SHM systems, involves the use of a single PZT for exciting and sensing of the host structure. In this paper, studies applied for the past decade related to the EMI technique have been reviewed to understand its trend. In addition, new concepts and ideas proposed by various authors are also surveyed, and the paper concludes with a discussion of the potential directions for future works.

Abstract Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

This study reviews the fundamental mechanisms of biological soil improvement methods—microbially induced calcium carbonate precipitation (MICP) and biopolymer treatment (BPT). Extensive experimental data on various geotechnical properties of sands treated by MICP and BPT are compiled, including the unconfined compressive strength, Mohr-Coulomb shear strength parameters, and permeability. Furthermore, the variations in these engineering parameters are correlated to calcium carbonate content for MICP and biopolymer content for BPT, which provides insights into the extent of biological modification in engineering properties of sands, potential applications, and limitations.

Abstract In this study, we report a surfactant-mediated synthesis of ferrites (MFe 2 O 4 : M = Co, Ni, Cu, Zn) using the co-precipitation-oxidation method. The band gap calculated from UV-Visible diffuse reflectance spectra were found in the range of 1.11–1.81 eV. These ferrite nanocatalysts were studied for the photocatalytic degradation of multiple organic dyes in a 32 W UV-C/H 2 O 2 system. All the four ferrites showed an excellent dye degradation rate in the range of 2.065–2.417 min −1 at neutral pH. In the optimized condition, NiF was found to degrade 89%, 92%, 93%, and 78% of methylene blue, methyl orange, bromo green, and methyl red, respectively within 1 min of UV-irradiation. A 40% TOC removal was recorded after 5 min of degradation reaction, which increased to 60% after 50 min. Mechanism elucidated by scavenger studies and fluorescence spectroscopy revealed that • OH and holes were the primary reactive radicals responsible for the degradation process. Ferrite photocatalysts showed an insignificant performance loss in seven consecutive cycles. The photocatalyst was found efficient in the presence of a high concentration of salts. Thus, it was concluded that these photocatalysts are highly suitable for the remediation of dye-contaminated wastewater.

Biological approaches have recently been explored as environmentally friendly alternatives to engineered soil methods in geotechnical engineering practices. The use of microbial induced calcite precipitation, reactive enzymes, and microbial polymers, such as biopolymers, in soil improvement has been studied by researchers around the world. In the present study, gellan gum, a microbial polysaccharide generally used in the food industry due to its hydrogel rheology, was used to strengthen sand. The effects of gellan gum on the geotechnical behaviors of cohesionless sand were evaluated through a series of experimental programs including an unconfined compression test, direct shear test, falling head permeability test, and scanning electron microscopy. The geotechnical properties (friction angle, cohesion, and unconfined compressive strength) of gellan gum–treated sands were determined based on varying moisture conditions: initial, dried, and re-submerged. Gellan gum has a distinct strengthening effect on cohesionless sands through artificial cohesion that varies with the moisture conditions. The strengthening effect of gellan gum on sand appears to be a result of the combination of enhanced bonding between unreactive sand particles and the agglomeration of sand particles through hydrogel condensation, in which the agglomerated sand particles behave as enlarged aggregates in soil.

Cracking in asphalt concrete pavements is a major form of pavement distress in the United States. Because the cracking phenomenon is complex and cracking is often affected by both material and structural factors, field engineers have no quick and effective test and analysis protocols. A suite of fatigue analysis tools—as well as applications built around the simplified viscoelastic continuum damage (S-VECD) model—is presented. The S-VECD formulation is presented in a summarized form. Next, the characterization protocols, which are consistent with the capabilities of the asphalt mixture performance tester, are shown. Considerable attention is then given to S-VECD–based analysis tools for assessment of material- and pavement-level fatigue performance. Results show that the S-VECD model can be used to predict the number of cycles until fatigue failure for both constant stress and constant strain loading. The S-VECD model's sensitivity to mixture composition and external factors is shown through predictions of the endurance limit. Finally, pavement performance predictions are used to show how the S-VECD model can predict the field performance results of full-scale accelerated pavement tests, quantify the expected performance of pavement design alternatives, and identify factors that affect top-down cracking.

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.

Abstract Using a 1951–2003 gridded daily rainfall dataset for India, the authors assess trends in the intensity and frequency of exceedance of thresholds derived from the 90th and the 99th percentile of daily rainfall. A nonparametric method is used to test for monotonic trends at each location. A field significance test is also applied at the national level to assess whether the individual trends identified could occur by chance in an analysis of the large number of time series analyzed. Statistically significant increasing trends in extremes of rainfall are identified over many parts of India, consistent with the indications from climate change models and the hypothesis that the hydrological cycle will intensify as the planet warms. Specifically, for the exceedance of the 99th percentile of daily rainfall, all locations where a significant increasing trend in frequency of exceedance is identified also exhibit a significant trend in rainfall intensity. However, extreme precipitation frequency over many parts of India also appears to exhibit a decreasing trend, especially for the exceedance of the 90th percentile of daily rainfall. Predominantly increasing trends in the intensity of extreme rainfall are observed for both exceedance thresholds.

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler’s experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.

In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.

The biological assessment of rivers i.e., their assessment through use of aquatic assemblages, integrates the effects of multiple-stressors on these systems over time and is essential to evaluate ecosystem condition and establish recovery measures. It has been undertaken in many countries since the 1990s, but not globally. And where national or multi-national monitoring networks have gathered large amounts of data, the poor water body classifications have not necessarily resulted in the rehabilitation of rivers. Thus, here we aimed to identify major gaps in the biological assessment and rehabilitation of rivers worldwide by focusing on the best examples in Asia, Europe, Oceania, and North, Central, and South America. Our study showed that it is not possible so far to draw a world map of the ecological quality of rivers. Biological assessment of rivers and streams is only implemented officially nation-wide and regularly in the European Union, Japan, Republic of Korea, South Africa, and the USA. In Australia, Canada, China, New Zealand, and Singapore it has been implemented officially at the state/province level (in some cases using common protocols) or in major catchments or even only once at the national level to define reference conditions (Australia). In other cases, biological monitoring is driven by a specific problem, impact assessments, water licenses, or the need to rehabilitate a river or a river section (as in Brazil, South Korea, China, Canada, Japan, Australia). In some countries monitoring programs have only been explored by research teams mostly at the catchment or local level (e.g., Brazil, Mexico, Chile, China, India, Malaysia, Thailand, Vietnam) or implemented by citizen science groups (e.g., Southern Africa, Gambia, East Africa, Australia, Brazil, Canada). The existing large-extent assessments show a striking loss of biodiversity in the last 2-3 decades in Japanese and New Zealand rivers (e.g., 42% and 70% of fish species threatened or endangered, respectively). A poor condition (below Good condition) exists in 25% of South Korean rivers, half of the European water bodies, and 44% of USA rivers, while in Australia 30% of the reaches sampled were significantly impaired in 2006. Regarding river rehabilitation, the greatest implementation has occurred in North America, Australia, Northern Europe, Japan, Singapore, and the Republic of Korea. Most rehabilitation measures have been related to improving water quality and river connectivity for fish or the improvement of riparian vegetation. The limited extent of most rehabilitation measures (i.e., not considering the entire catchment) often constrains the improvement of biological condition. Yet, many rehabilitation projects also lack pre-and/or post-monitoring of ecological condition, which prevents assessing the success and shortcomings of the recovery measures. Economic constraints are the most cited limitation for implementing monitoring programs and rehabilitation actions, followed by technical limitations, limited knowledge of the fauna and flora and their life-history traits (especially in Africa, South America and Mexico), and poor awareness by decision-makers. On the other hand, citizen involvement is recognized as key to the success and sustainability of rehabilitation projects. Thus, establishing rehabilitation needs, defining clear goals, tracking progress towards achieving them, and involving local populations and stakeholders are key recommendations for rehabilitation projects (Table 1). Large-extent and long-term monitoring programs are also essential to provide a realistic overview of the condition of rivers worldwide. Soon, the use of DNA biological samples and eDNA to investigate aquatic diversity could contribute to reducing costs and thus increase monitoring efforts and a more complete assessment of biodiversity. Finally, we propose developing transcontinental teams to elaborate and improve technical guidelines for implementing biological monitoring programs and river rehabilitation and establishing common financial and technical frameworks for managing international catchments. We also recommend providing such expert teams through the United Nations Environment Program to aid the extension of biomonitoring, bioassessment, and river rehabilitation knowledge globally.

Abstract A new 10-category, polarimetric-based hydrometeor identification algorithm (HID) for C band is developed from theoretical scattering simulations including wet snow, hail, and big drops/melting hail. The HID is applied to data from seven wet seasons in Darwin, Australia, using the polarimetric C-band (C-POL) radar, to investigate microphysical differences between monsoon and break periods. Scattering simulations reveal significant Mie effects with large hail (diameter > 1.5 cm), with reduced reflectivity and enhanced differential reflectivity Z dr and specific differential phase K dp relative to those associated with S band. Wet snow is found to be associated with greatly depreciated correlation coefficient ρ hv and moderate values of Z dr . It is noted that large oblate liquid drops can produce the same electromagnetic signatures at C band as melting hail falling quasi stably, resulting in some ambiguity in the HID retrievals. Application of the new HID to seven seasons of C-POL data reveals that hail and big drops/melting hail occur much more frequently during break periods than during monsoon periods. Break periods have a high frequency of vertically aligned ice above 12 km, suggesting the presence of strong electric fields. Reflectivity and mean drop diameter D 0 statistics demonstrate that convective areas in both monsoon and break periods may have robust coalescence or melting precipitation ice processes, leading to enhanced reflectivity and broader distributions of D 0 . Conversely, for stratiform regions in both regimes, mean reflectivity decreases below the melting level, indicative of evaporative processes. Break periods also have larger ice water path fractions, indicating substantial mixed-phase precipitation generation as compared with monsoonal periods. In monsoon periods, a larger percentage of precipitation is produced through warm-rain processes.

A three-dimensional (3-D) finite element (FE) model was developed to predict pavement responses to vehicular loading. The model incorporates measured tire-pavement contact stresses, continuous moving wheel loading, and hot-mix asphalt (HMA) viscoelastic characteristics. The model was fine-tuned using implicit-dynamic analysis and validated using pavement response from accelerated loading. Two tire configurations (dual-tire assembly and wide-base 455 tire) and three full-depth flexible pavement designs (HMA 152 mm, 254 mm, and 420 mm) were used in both FE modeling and accelerated loading tests. The predicted and calculated strain responses at the bottom of HMA were in agreement. Most important, the study shows that vertical shear strain in the upper 76 to 100 mm of the pavement surface is critical for thick pavement and is influenced by the 3-D tire-pavement contact stresses under each tire rib. However, the tensile strain at the bottom of HMA is affected mainly by the total wheel load. The vertical shear strain is responsible for near-surface fatigue cracking as well as HMA primary rutting. Top-down cracking could result from the local vertical shear strain in the upper 25 mm of the HMA where the effect of tire-pavement tangential stresses are the highest. In addition, the study concluded that wide-base tires cause higher longitudinal tensile strain at the bottom of HMA and compressive strain at the top of subgrade, where those responses are highly affected by the total wheel load. However, wide-base tires were found to cause less vertical shear strains near the surface than dual-tire assembly loading regardless of HMA thicknesses.

Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on two-dimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.