Experimental study on the high-velocity impact behavior of sandwich structures with an emphasis on the layering effects of foam core

In this study, the effects of the core layering of sandwich structures, as well as arrangements of these layers on the ballistic resistance of the structures under high-velocity impact, were investigated. Sandwich structures consist of aluminum face-sheets (AL-1050) and polyurethane foam core with different densities. Three sandwich structures with a single-layer core of different core densities and four sandwich structures with a four-layer core of different layers arrangements were constructed. Cylindrical steel projectiles with hemispherical nose, 8 mm diameter and 20 mm length were used. The projectile impact velocity range was chosen from 180 to 320 m/s. Considering constant mass and total thickness for the core, the results of the study showed that the core layering increases the ballistic limit velocity of the sandwich structures. The ballistic limit velocity of the panels with a four-layer core of different arrangements, compared to the panel with the single-layer core, is higher from 5% to 8%. Also, for the single-layer core structure, by increasing the core density, the ballistic limit velocity was increased. Different failure mechanisms such as plugging, petaling and dishing occurred for the back face-sheet. The dishing area diameter of back face-sheets was proportional to the ballistic resistance of each sandwich structure

The Influence of Geometrical Shape Changes on Wave Overtopping: a Laboratory and SPH Numerical Study

This paper presents laboratory investigations of four “retrofit” suggestions for attenuating the overtopping from vertical seawall. Two-dimensional physical model experiments were performed on a vertical seawall with a 1:20 sloping foreshore. Additionally, a Lagrangian, particle based SPH methodology was employed to simulate the wave hydrodynamics and overtopping for the recurve configuration. The experimental and numerical results confirm satisfactory performance. For the tested configurations in the laboratory, the mean overtopping discharges decreased over 60% and maximum individual discharge decreased 40% on recurve wall under both impulsive and non-impulsive conditions. A significant reduction was also observed in mitigating overtopping discharge by using model vegetation and reef breakwater, while diffraction pillar was not found satisfactory

Application of smoothed particle hydrodynamics in evaluating the performance of coastal retrofit structures

This study develops an accurate numerical tool for investigating optimal retrofit configurations in order to minimize wave overtopping from a vertical seawall due to extreme climatic events and under changing climate. A weakly compressible smoothed particle hydrodynamics (WCSPH) model is developed to simulate the wave-structure interactions for coastal retrofit structures in front of a vertical seawall. A range of possible physical configurations of coastal retrofits including re-curve wall and submerged breakwater are modelled with the numerical model to understand their performance under different wave and structural conditions. The numerical model is successfully validated against laboratory data collected in 2D wave flume at Warwick Water Laboratory. The findings of numerical modelling are in good agreement with the laboratory data. The results indicate that recurve wall is more effective in mitigating wave overtopping and provides more resilience to coastal flooding in comparison to base-case (plain vertical wall) and submerged breakwater retrofit

Longitudinal dispersion of microplastics in aquatic flows using fluorometric techniques

Microplastics are an emerging environmental contaminant. Existing knowledge on the precise transport processes involved in the movement of microplastics in natural water bodies is limited. Microplastic fate-transport models rely on numerical simulations with limited empirical data to support and validate these models. We adopted fluorometric principles to track the movement of both fluorescent dye and florescent stained microplastics (polyethylene) in purpose-built laboratory flumes with standard fibre-optic fluorometers. Neutrally buoyant microplastics behaved in the same manner as a solute (Rhodamine) and more importantly displayed classical fundamental dispersion theory in uniform open channel flow. This suggests Rhodamine, a fluorescent tracer, can be released into the natural environment with the potential to mimic microplastic movement in the water column

The Effect of Cognitive–Behavioral Group Therapy on Menopausal Symptoms

BACKGROUND AND OBJECTIVE: The common symptoms of menopause are associated with anxiety and discomfort for most women, and this is one of the major healthcare challenges. The aim of this study was to evaluate the effect of cognitive – behavioral group therapy on menopausal symptoms (primary outcome). METHODS: This randomized controlled clinical trial was conducted among 90 menopausal women with health records at two health centers in Tuyserkan in 2016 and were randomly assigned to two groups of intervention and control (45 patients in each group). To perform cognitive – behavioral group therapy, six 90-minute sessions were held for the intervention group for six consecutive weeks. Menopausal symptoms were discussed in each of these sessions based on cognitive techniques such as identifying negative automatic thoughts and behavioral techniques such as diaphragmatic breathing technique. Menopausal symptoms were assessed in both groups using the Greene Climacteric Scale (0 – 63) before the intervention and at the end of the sixth week. To adhere to ethics, the control group received one session of educational counseling after the assessments were done. FINDINGS: There was no statistically significant difference in mean total Greene score between the cognitive– behavioral group (22.78±12.22) and control group (24.8±10.25) before intervention. After the intervention, the mean total Greene score decreased significantly in the cognitive – behavioral group (15.75±7.24) compared to the control group (24.97±9.25) (p < 0.05). CONCLUSION: The results showed that cognitive – behavioral group therapy can decrease menopausal symptoms

Improved prediction of wave overtopping rates at vertical seawalls with recurve retrofitting

This study investigates the reduction in overtopping discharge along a vertical seawall through the implementation of a recurve retrofitting. A comprehensive set of physical modelling experiments were undertaken in a laboratory-scale wave flume at the University of Warwick, to investigate the wave overtopping processes under both swell and storm wave conditions. The tests measured overtopping discharges for impulsive and non-impulsive wave conditions. The effects of geometrical design of recurve retrofitting on overtopping reduction are examined by four configurations with varying overhang length and recurve hight. The study revealed that the reduction in overtopping is primarily determined by the length of the overhang in the recurve wall, while the influence of the recurve height is limited. A longer overhang length results in a more substantial decrease in overtopping discharges on the seawall crest. The results also highlight the role of incident wave steepness and the crest freeboard on the overtopping mitigation performance of the recurve walls. A new enhanced methodology is proposed to predict the wave overtopping from vertical seawalls with recurve retrofitting., considering the effects of freeboard and wave steepness. The findings of this study provide new important insight in the role of retrofitting as a robust intervention to improve the wave overtopping mitigation performance of seawalls. The predictive empirical formulae proposed by this study facilitate readily and accurate estimation of overtopping rates as a function of retrofitting geometrical design, allowing for wider application of retrofitting solutions

Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms

Accurate prediction of wave overtopping at sea defences remains central to the protection of lives, livelihoods, and infrastructural assets in coastal zones. In addressing the increased risks of rising sea levels and more frequent storm surges, robust assessment and prediction methods for overtopping prediction are increasingly important. Methods for predicting overtopping have typically relied on empirical relations based on physical modelling and numerical simulation data. In recent years, with advances in computational efficiency, data-driven techniques including advanced Machine Learning (ML) methods have become more readily applicable. However, the methodological appropriateness and performance evaluation of ML techniques for predicting wave overtopping at vertical seawalls has not been extensively studied. This study examines the predictive performance of four ML techniques, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machines—Regression (SVR), and Artificial Neural Network (ANN) for overtopping discharge at vertical seawalls. The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. Feature Transformation and advanced Feature Selection methods are adopted to reduce data redundancy and overfitting. Comprehensive statistical analysis shows superior performance of the RF method, followed in turn by the GBDT, SVR, and ANN models, respectively. In addition to this, Decision Tree (DT) based methods such as GBDT and RF are shown to be more computationally efficient than SVR and ANN, with GBDT performing simulations more rapidly that other methods. This study shows that ML approaches can be adopted as a reliable and computationally effective method for evaluating wave overtopping at vertical seawalls across a wide range of hydrodynamic and structural conditions

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making

R : Prediction of wave overtopping rates at sloping structures using artificial intelligence

The prediction of wave overtopping at coastal defenses is critical to ensure the flood resilience of people and properties in low-lying nearshore coastal areas. With the effects of anthropogenic climate change, the frequency of wave overtopping is expected to increase, along with sea level rise and more frequent damaging storm surges. Established approaches for the prediction of wave overtopping have traditionally relied on physical and numerical modelling and empirical methods. The ubiquity of computational resources has led to the emergence of Artificial Intelligence techniques, such as Machine Learning (ML) algorithms, as a promising approach for predicting wave overtopping. This study investigates the application of four ML models based on Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machines Regression (SVMR) and Artificial Neural Networks (ANN) approach for predicting wave overtopping at sloping breakwaters. Data from the EurOtop II manual, a comprehensive dataset of physical and numerical wave overtopping tests undertaken on a variety of coastal structure geometries, including sloping breakwaters (the focus of this study), underpinned the developed models.. To optimize the data for redundancy, feature transformation and advanced feature selection methods were employed. Hyperparameter tuning was performed to extract the best features for the predictive models. The performance of the developed ML-based models was examined in terms of the coefficient of determination, r2, and the Pearson correlation coefficient, R, for the measured and predicted overtopping values. The range of r2 values across the four models varied between 0.69 to 0.87, with Pearson correlations varying between 0.87 and 0.93. The results show that the GBDT model outperformed the other ML models tested in this study

Eco-engineering of seawalls—an opportunity for enhanced climate resilience from increased topographic complexity

In the context of “green” approaches to coastal engineering, the term “eco-engineering” has emerged in recent years to describe the incorporation of ecological concepts (including artificially water-filled depressions and surface textured tiles on seawalls and drilled holes in sea structures) into the conventional design process for marine infrastructures. Limited studies have evaluated the potential increase in wave energy dissipation resulting from the increased hydraulic roughness of ecologically modified sea defences which could reduce wave overtopping and consequent coastal flood risks, while increasing biodiversity. This paper presents results of small-scale laboratory investigations of wave overtopping on artificially roughened seawalls. Impulsive and non-impulsive wave conditions with two deep-water wave steepness values (=0.015 and 0.06) are evaluated to simulate both swell and storm conditions in a two-dimensional wave flume with an impermeable 1:20 foreshore slope. Measurements from a plain vertical seawall are taken as the reference case. The seawall was subsequently modified to include 10 further test configurations where hydraulic effects, reflective of “eco-engineering” interventions, were simulated by progressively increasing seawall roughness with surface protrusions across three length scales and three surface densities. Measurements at the plain vertical seawall compared favorably to empirical predictions from the EurOtop II Design Manual and served as a validation of the experimental approach. Results from physical model experiments showed that increasing the length and/or density of surface protrusions reduced overtopping on seawalls. Benchmarking of test results from experiments with modified seawalls to reference conditions showed that the mean overtopping rate was reduced by up to 100% (test case where protrusion density and length were maximum) under impulsive wave conditions. Results of this study highlight the potential for eco-engineering interventions on seawalls to mitigate extreme wave overtopping hazards by dissipating additional wave energy through increased surface roughness on the structure