This paper applies the meta-modeling theory proposed by the authors to a thin curved beam. The key features which are new to the curved beam problem are the introduction of a new curvilinear coordinate system inherent to the beam configuration and the asymptotic expansion using the ratio of the beam thickness to the beam radius. The governing equations for displacement are derived from a Lagrangian of continuum mechanics, making error-free computation of the covariant derivative and systematic computation of the asymptotic expansion. Discussions are made on the derivation to clarify the significance of the two key features.

The assessment of corrosion damage is an essential part of the maintenance of steel structures. The numerical analysis of the properties of corrosion surfaces and the accurate prediction of corrosion surfaces are of great significance. In this study, four kinds of unpainted steel plates, SM400A, SM490A, SMA400AW, and SMA490AW, were used for corrosion experiments under the artificial seawater corrosive environment ISO 16539 Method B, and two atmospheric exposure environments in different regions. The corrosion depths of the steel plates were measured by a laser focus measurement system. Semi-variogram was used in the geostatistical analysis to investigate the spatial autocorrelation structure of the corrosion surfaces. By using this method and the ordinary kriging technique, a method was proposed to simulate the spatial characteristics of the corrosion surfaces. The simulation results indicated that the corrosion depth and surface morphology of the corrosion surface were in high agreement with the experimental results. In addition, a deep learning model based on generative adversarial network (GAN) was used to build a prediction model of the corrosion surface. The spatial properties of the prediction model were verified using the geostatistical analysis method proposed in this study, and the results showed that the predictions had the same spatial properties as the actual corrosion surface.

A computation method was proposed for the interactions between Newtonian fluids and deformable solid objects which swell by absorbing the surrounding flids. The direct-forcing immersed boundary method and mass-spring model are used to estimate the fluid-solid interactive forces and deformations of the solid. The swelling of the object is simulated by changing the natural lengths of the spring models. In addition, the solid-solid interaction is treated by utilizing the distinct element method. The proposed method was applied to three numerical experiments. As a result, it was shown that the basic behaviors of the swelling-deformable objects are reasonably calculated with the present method.

This study is an extension based on our previous work ^1,2), in which we mainly discussed the µ(I) rheological model for quasi-monodisperse circular and elliptic systems. In this paper, we numerically study the inclined plane flows composed of two-sized circular particles through the 2D discrete element method. By carrying out simulations with different size ratios S_r , bulk volume ratios V_r , and slope angles θ (altogether 125 different systems), we discuss how this rheological model works for bi-disperse systems in detail. It is found that (1) the µ(I)-rheology model is still valid for the bi-disperse flows of different size ratios S_r and volume ratios V_r; (2) the contact type (large to large, small to small, and large to small particles) proportions are not dependent on the shear rates, which enables us to formulate them for the definition of a generalized inertial number Î; (3) the dependence of fitting parameters, including the mean effective friction coefficients µ₀ and solid fraction φ₀ in the quasistatic regime, on the system bi-dispersity (i.e., S_r and V_r) is discussed and clarified.

In the recent flood disaster, the remarkable water level rise in river flows over a short period led to the levee breaching, which caused severe damage in the landside region. Therefore, numerical methods that can predict flood flows and bed deformation around a levee are of great significance for flood mitigation. In this study, a three-dimensional (3D) numerical model of open channel flows in a boundary fitted coordinate system with the density function method is used, and by comparing the hydraulic characteristics of simulation, such as water level and velocity distributions, to the previous experiments of lateral overtopping flows around a side weir in the curved channel, the reliability of the numerical model is validated and three-dimensional flow structures around side weir have also been investigated.

 Onshore tsunami height is often forecasted or estimated by multiplying an amplification factor by the calculated tsunami height at corresponding offshore sampling points, especially for scenario-based tsunami databases used for real-time tsunami forecasting. However, the maximum tsunami heights at the offshore sampling points are often affected by waves reflected from the shore. To reduce the overestimation or underestimation of the coastal tsunami amplitude, a simplified corrected maximum tsunami height (SCTH), which is the root mean square of the maximum tsunami height and normalized maximum velocity, is defined in this study. After numerical tests and a case study of the tsunami caused by the earthquake that occurred in Fukushima Prefecture on November 22, 2016, SCTH has been confirmed to reduce prediction errors caused by minor differences in the sampled location when applying an empirical relation on coastal tsunami heights between offshore and corresponding onshore points.

 In 1977, the Minamata Bay Pollution Prevention Project was initiated to dispose of sedimentary sludge containing over 25 ppm of total mercury (T-Hg) after the outbreak of Minamata Disease. The sediments containing the high concentration Hg were dredged, but nevertheless, the residual Hg in Minamata Bay has attracted much attention. Moreover, some studies have even indicated that the Hg remaining in the sediments near the bay has migrated to the Yatsushiro Sea.

 In this study, the vertical distribution of mercury concentration across sediment layers at different sites was investigated. Developed numerical modeling was used to evaluate the impact of sediment particle size on the migration of Hg-containing sediments from Minamata Bay to the Yatsushiro Sea as well. According to the results, mercury migrated from Minamata Bay to the Yatsushiro Sea, where the mercury migrating to the southwest of the Yatsushiro Sea is more concentrated. Additionally, there were significant differences in mercury concentrations at various depths and locations. The total mercury content varied with the particle size. The migration of mercury-containing sediments with larger particle size was slower and the migration range was limited.

 After the outbreak of Minamata Disease, highly mercury-polluted sediment was dredged from Minamata Bay. Nevertheless, some studies indicated that trace mercury (Hg) migrated from Minamata Bay to the Yatsushiro Sea with the movement of sediments. Mercury was considered to adsorb on sediment particles and realized the transport. However, the influence of particle size as a crucial factor affecting adsorption is barely considered in the analysis of mercury migration.

 In this study, we develop a novel method based on particle size classification to comprehend both horizontal and vertical distribution of the total-Hg (T-Hg) concentration in sediments with different particle sizes in the Yatsushiro Sea. From the experiment result, it could be concluded that overall, in the horizontal direction, the T-Hg concentration became smaller and smaller along A and B lines from the sampling point Y28, indicating the mercury transport from Minamata Bay to the Yatsushiro Sea. Moreover, the T-Hg concentration increased first and then decreased in the vertical direction, suggesting that the migrated mercury was mainly deposited in the upper sediments. Furthermore, according to the classification experiment results, the smaller the particle size, the higher the T-Hg concentration. And, the sediment particles migrating from Minamata Bay to the Yatsushiro Sea are mainly very fine slit to fine silt.

 XBeach is a numerical model for simulating coastal processes, including beach morphodynamics, dune evolution, and overwash processes. It is designed to simulate the response of the beach to various environmental factors, including waves. XBeach uses advanced numerical algorithms to solve the equations of motion and sediment transport and is capable of modeling the complex interactions between the ocean and the beach. Landward scour is the sediment’s hole behind the structure which is induced by overflowing flow during the extreme event, such as tsunamis and storms, which have high water depth and large velocity flow. This research is about modeling of landward scour phenomenon. The simulation results were compared against experimental results. This study shows that XBeach can model the morphodynamics shape of the scour hole. However, the scour depth as also suggested by other researchers for extreme overflowing setting is overestimated. Hydrodynamics parameters (water level) can be reproduced by the model with good agreement against the experiment although simulated flow velocity is slightly overestimated. It was also found that in a very short time scale phenomenon, as in the landward scour experiment, the model is sensitive to governing equation of sediment transport. An attempt to enhance the result by implementing artificial erosion limiter by using soil dilatancy improves the results, especially for erosion volume (V_err).

 This study presents the effect of sediment size on the morphological change of a sandbar under loose and medium-dense compaction. An artificial cross-shore beach profile was designed with a small-scale sandbar on the offshore side of a 2D wave flume. A total of 12 regular wave cases were tested on two different sandbars setup using medium sand (D₅₀ = 0.33 mm) and very fine sand (D₅₀ = 0.08 mm). Experimental results revealed that very fine sand was more susceptible to erosion than medium sand in the mechanism of wave breaking to impinging. The maximum erosion zone for medium and very fine sand was found at the offshore sharp edge of the sandbar and at the impinging point, respectively. In loose compaction, a strong inverse correlation (R² = 0.80) was found between the net erosion volume and wave steepness for the medium sand, whereas a sharp peak of net erosion volume existed at the medium wave steepness in very fine sand. In medium-dense compaction of very fine sand, the reduction of net erosion volume was 67 %, 57 %, and 82 % at low, medium, and high wave steepness, respectively. At the middle depth of the sandbar, very fine sand yielded an average increase in shear strength that was five times higher than medium sand. At this depth, a strong inverse correlation (R² = 0.84) was established between the change in average shear strength and wave steepness under loose compaction of medium sand, however, similar to net erosion volume, a sharp peak was found at the medium wave steepness of very fine sand. Furthermore, datasets assessed in the laboratory would be useful for numerical simulation.

 This study investigates the potential of Artificial Neural Networks (ANN) in predicting beach vulnerability to storm-induced erosion. Long-term morphology and hydrodynamic data (24 years) from Hasaki beach in Japan were used to identify storm events and quantify beach erosion. First, to compare the performance of an ANN model with a Multiple Linear Regression (MLR) model in predicting the shoreline change (dSL) during storms, we used initial shoreline position, storm power, maximum surge, and beach slope as input variables. Next, the model predictions of the dSL were utilized to quantify the beach vulnerability on a scale of 1 to 5, resulting in the creation of the Beach Vulnerability Index: BVI_ANN for the ANN model and BVI_MLR for the MLR model. While MLR performed well for short-term beach erosion predictions (8 years) as Thilakarathne et al. (2022) showed, our results indicate that it was less effective when using long-term storm data. In contrast, ANN demonstrated superior performance, resulting in more accurate predictions of beach vulnerability. Specifically, the Mean Absolute Errors for BVI_MLR were 1.33, 0.83, 0.78, 0.90, and 1.07, while for BVI_ANN were 1.00, 0.20, 0.69, 1.05, and 0.57 for indexes 1-5, respectively. The ANN model also achieved higher R² Scores for both training (0.65) and testing (0.62) data in predicting dSL, compared to the MLR model (0.26 on training and 0.35 on testing). The study findings suggest that using ANN or other Machine Learning (ML)-based algorithms for coastal/beach vulnerability studies has significant potential for capturing and representing the dynamic nature of beach morphology changes with increased accuracy. The study's contribution adds to the growing body of research on using ANN and ML algorithms for predicting coastal morphology changes and beach vulnerability, highlighting the potential of these methods for future coastal engineering applications.

 This study further develops the earthquake–tsunami interaction diagram by considering two different limit states simultaneously: inter-layer deformation angle and foundation uplift. The study considered a three-story building on a mat foundation as a building typically damaged by tsunami. Six different variously scaled ground motions recorded during the 2011 Tohoku earthquake and a tsunami hydrodynamic force were applied to generate the diagram. The results indicate that the resulting dominant limit state depends on the characteristics of the input ground motions.

 In developing countries including Ethiopia, potholes are one of the most serious problems of paved roads, especially after the rainy season. Thus, a criterion that enables road administrators to prioritize the maintenance for pavements with potholes is required in addition to common indices such as the International Roughness Index (IRI). The IRI is used as a standard indicator as it can show the average deviation of a road surface. However, the use of only IRI possibly leads to misinformation when the pavement has been damaged with severe potholes, which causes traffic accidents as well as high user costs. This is due to the conditions of which a pothole develops at some locations of the pavement while no damages are observed in the rest of them. If the condition of potholes is without consideration into IRI-based pavement inspection, it brings inappropriate result and then the road users complain. In the light of this background, this study examines the impact of pothole on road user response in terms of driving safety and comfort for pavement maintenance prioritization. For this purpose, artificial pothole data at different intervals such as 10 m, 20 m, 40 m, and one pothole in a 100 m segment are considered as well as the IRI for the segment. In this study, depths of 25 mm, 50 mm, 75 mm, and 100 mm are simulated for paved roads with two layers of asphalt which is a common case in Addis Ababa City Roads. In addition to the depth and interval, the pothole diameters such as 200 mm, 500 mm, and 750 mm are also involved as a parameter. This study contributes to the establishment of a criterion for pothole conditions that are used in conjunction with the IRI for the prioritization of pavement maintenance, especially in developing countries.

 Due to the big challenge to rapid start-up of full-scale anammox (anaerobic ammonium oxidation) processes, preserving active anammox biomass has been paid more and more attention. In this study, Anammox-HAP granules were cultured in an expanded granular sludge bed (EGSB) reactor, and then the granules were preserved at different methods during 1 d to 6 years.

 There are 4 different storage methods have been used for short-term storage. The results proved that the most economical method of storage for one week was room temperature without media, where about 80% of the specific anammox activity (SAA) is retained. For more than one month, the effect of low temperature storage at 4℃ is significantly better than that of room temperature storage. As to the storage of more than three months, 4℃ and molybdate addition had better preservation effect on SAA. During the preservation period, a first order exponential decay model may be used to simulate the decay of the anammox activity.

 The results showed that Anammox-HAP granules still retained a high biomass and settling velocity during long-term storage in 6 years, and SAA retained more than 50% after half a year of storage. The results demonstrated that, compared with the ordinary anammox granular sludge, Anammox-HAP granules had better active and biomass preservation ability and retained its own superiority after long-term storage, which provides the feasibility for quick start-up of anammox process.

 This research presents a comprehensive analysis of the long-term performance and energy efficiency of a pilot-scale Self Agitation-Anaerobic Baffled Reactor (SA-ABR) in treating swine wastewater, that aimed to investigate the impact of seasonal variations on the reactor's performance, focusing on the removal efficiency of organic pollutants, biogas production rate, and energy input-output analysis. Highest COD removal efficiency of 69.6% was achieved in summer and lowest efficiency of 39.2% was achieved in winter. Additionally, an equilibrium relationship between energy yield output and consumption is established to provide insights into the optimal operation and design of the SA-ABR reactor for practical applications. This study can provide reference for the application of full-scale SA-ABR in the treatment of actual wastewater in the future, such as temperature control and other aspects.

 This paper explores the applicability of estimating methane emission from cows using non-contact sensors and deep learning techniques. The study was conducted on a Holstein cow housed in a tie-stall barn at Rakuno Gakuen University in Ebetsu-shi, Hokkaido, Japan. Methane concentration in the cow's breath and vital data, including heart rate, respiratory rate, and body movements, were measured using a laser methane detector (LMD) and a non-contact sensor, respectively. The LMD data was preprocessed to “extracted Mini-peaks”, which represent exhalation events, to be used as the target variable dataset. In addition, the vital data was used as the explanatory variable dataset. The Long Short-Term Memory (LSTM) model was implemented to estimate methane concentration in a cow’s breath, and the performance of the model was evaluated based on the root mean square error (RMSE) value. The results showed that the LSTM model trained with the “extracted Mini-peaks” data outperformed the model trained with the “raw LMD” data, indicating that the Mini-peaks were more closely related to vital data. Furthermore, the LSTM model trained with the “extracted Mini-peaks” data exhibited a relative error ranging from 1.9% to 11.6% in estimating daily methane emissions, compared to that calculated from observed methane concentration. The study demonstrated the applicability of estimating methane emissions from cows using non-contact sensors and the LSTM model with achieving estimation accuracy that is comparable to the LMD method. This approach could provide a cost-effective and efficient method for monitoring methane emissions from cows, contributing to the development of sustainable livestock farming practices.

 Burkina Faso, a Sahelian country, has witnessed an increase in flooding events over the last few decades. With increasing concerns about climate change, the frequency and severity of flooding events in the country are expected to rise. Effective flood risk mitigation requires information collection in relation with socioeconomic factors like poverty. However, while previous studies have explored the association between floods and poverty at the intra-urban and household levels there is still limited understanding of this association on regional and national scales, especially in a country with data constraints. Therefore, this study aimed to provide valuable insights by mapping flood-sensitive areas, quantifying potentially exposed populations and investigating the relationship between poverty and flood exposure in Burkina Faso through a poverty index. To achieve these objectives, Geographical Information System (GIS) tools were employed with remote sensing data, including nighttime lights and flood extent. In 20 years, the number of people potentially affected by floods increased by 43.9 % on a national scale. Sahel, North-Central, Boucle-duMouhoun, and North regions would be the most vulnerable to a 50 years return period target flood. Areas with higher proportions of poverty were South-Central, North-Central, North, and Sahel following the results of the poverty index between 2000 and 2015. The relationship between floods and poverty was confirmed on a national and regional scale and most of the flood-affected regions also happen to have a high poverty index. Furthermore, this study sheds light on the vulnerability of the northern regions of Burkina Faso.

 Landslide susceptibility being a severe threat abundant worldwide, they tend to occur in a variety of geological and environmental settings, while imposing devastating consequences for both human life and infrastructure. Characterized by a unique combination of geological and climatic factors that are conducive to slope instability, the central highlands of Sri Lanka, are more frequently exposed to the risk of slope failures compared to other regions in the country. Provided the necessity of comprehensive knowledge and understanding of the related hydro-geological phenomena underlying the specific geological setting, developing a deterministic landslide model finds a high level of applicability and usability in disaster risk mitigation strategies, which sets the objective of this research. The study enabled the development of an integrated numerical model within the geographical information system environment, to simulate the regional hydro-geological processes to predict the groundwater levels and the associated measures of slope stability to map the distribution of landslide susceptibility within the region. Backed by a recovery rate of 79% in predicted water table elevations, the model’s predictions on slope stability were in agreement with a known past landslide occurrence within the period of simulation. The developed model with enhanced features of adaptability, being the key outcome, this research is aimed at setting an initiative that with the potential to be developed into an advanced modeling environment for landslide susceptibility evaluations, enabling effective decision-making in the future.

 Frequent severe flood occurrences in Rathnapura city, Sri Lanka cause damages to both human lives and infrastructures. The existing flood mapping models have certain shortcomings that could be enhanced by utilizing more advanced and combined approaches. In this research, we suggested a new technique to enhance the prediction accuracy of flood susceptibility mapping by integrating the bivariate index of entropy (IoE) and support vector machine (SVM) as an ensemble method. The suggested approach was developed using four SVM kernels; polynomial, linear, sigmoid and radial basis function to examine the robustness of predictability of SVM technique. First a flood inventory map with 445 flood locations was created using satellite images, field survey and documentary sources. A spatial database was created with eight flood conditioning factors(FCFs) including altitude, slope, aspect, topographic roughness index(TRI), soil, Land use, distance from river and rainfall. Initially, IoE was utilized to assess the correlation between the various flood conditioning factors and flood occurrence. Afterwards, the results of the first step were utilized to perform SVM model. Model validation was carried out using seed cell area index(SCAI) and area under curve(AUC) methods. The highest success and prediction rates of 94.82% and 95.81% and lowest SCAI values of 0.18, 0.878 for very high and high susceptibility classes were achieved by ensemble IoE-SVM(RBF) model. Out of all the methods used, lowest accuracies were obtained by the individual IoE and SVM(RBF) method. Ensemble methods, enhance flood prediction capabilities and conditioning factor evaluation resulting in a more accurate final map.