Experimental Investigation on the Structural Performance of Single Span Hollow Core Slab under Successive Impact Loading

In Lebanon and many other countries where structures are vulnerable to impact loads caused by accidental rock falls due to landslides, specifically bridges with hollow core slab, it is mandatory to develop safe and efficient design procedures to design such types of structures to withstand extreme cases of loading. The structural response of concrete members subjected to low velocity high falling weight raised the interest of researchers in the previous years. The effect of impact due to landslide falling rocks on reinforced concrete (RC) slabs has been investigated by many researchers, while very few studied the effect of impact loading on pre-stressed structures, noting that a recent study was conducted at Beirut Arab University which compared the dynamic behavior of reinforced concrete and post-tensioned slabs under impact loading from a 605 kg impactor freely dropped from a height of 20 m. Hollow core slabs are widely used in bridges and precast structures. Thus, studying their behavior due to such hazards becomes inevitable. This study focuses on these types of slabs. For a better understanding of the behavior, a full scale experimental program consists of testing a single span hollow core slab. The specimen has 6000 mm × 1200 mm × 200 mm dimensions with a 100 mm cast in a place topping slab. Successive free fall drops cases from 14 m height will be investigated on the prescribed slab having a span of 6000 m. This series of impacts will be held by hitting the single span hollow core slab at three different locations: center, edge, and near the support. The data from the testing program were used to assess the structural response in terms of experimental observations, maximum impact and inertia forces, structural damage/failure: type and pattern, acceleration response, and structural design recommendations. This research showed that the hollow core slab has a different dynamic behavior compared to the post tensioned and reinforced concrete slabs mentioned in the literature review section.

PREDICTION OF HIGH-PERFORMANCE FIBERREINFORCED POLYMER CONCRETE USING FUZZY NEURAL NETWORK PROTOTYPES

RC building elements of Reinforcing and upgrading is essential to extend its maintenance time, to overcome first structural limitations, and to control the consequence of building construction or design flaws. The RC constructions are reinforced by using the FRP-fiber reinforced polymer. This study utilizes the FRP in concrete structures for instance a Jute, coir, and Sisal is explored for its reliability in improving ductility and strength related structural performance. FRP structural response of the model parameters is studied by measuring the numerical and experimental terms, for instance, Ductility, Deflection, Tensile-Strength, and Compression-Strength. The quality of the sample specimens is tested by using the Fuzzy Neural Network (FNN) system. At this time, compared with existing jobs, the propounded Fuzzy Neural Network model accomplishes the best presentation regarding all boundaries for the fiberreinforced specimen over different stacked conditions

Evaluation of the Lateral Vibration Response of Footbridges under Uncertainty Conditions

<p>The evaluation of the vibration performance of footbridges due to walking pedestrians is an issue of increasing importance in current footbridge design practice. The growing trend of slender footbridges with long spans and light materials has led to serviceability problems in lateral vibrations, which occur when the number of pedestrians reaches a “critical number”. Considering the mode of vibration in which the lateral instability is more likely to develop, the structural response depends on the modal characteristics of the footbridge; in particular, the natural frequency and the damping ratio. These modal parameters are stochastic variables, as it is not possible to determine them without a level of uncertainty. Thus, the purpose of this paper is to obtain the value of the lateral dynamic response of slender footbridges with a certain confidence level under uncertainty conditions. The uncertainties of those modal parameters are considered using a probabilistic approach. Both the natural frequency and the damping ratio are modelled as uncorrelated random variables that follow a predetermined probabilistic distribution function. Consequently, the structural response will also be described by a probabilistic distribution function, which can be estimated through Monte Carlo numerical simulations. As a result, the study allows the footbridge lateral response and the critical number of pedestrians to be calculated for different confidence levels and load scenarios, especially for crowd densities above the “critical number”.</p>

On the importance of the electric double layer structure in aqueous electrocatalysis

To design electrochemical interfaces for efficient electric-chemical energy interconversion, it is critical to reveal the electric double layer (EDL) structure and relate it with electrochemical activity; nonetheless, this has been a long-standing challenge. Of particular, no molecular-level theories have fully explained the characteristic two peaks arising in the potential-dependence of the EDL capacitance, which is sensitively dependent on the EDL structure. We herein demonstrate that our first-principles-based molecular simulation reproduces the experimental capacitance peaks. The origin of two peaks emerging at anodic and cathodic potentials is unveiled to be an electrosorption of ions and a structural phase transition, respectively. We further find a cation complexation gradually modifies the EDL structure and the field strength, which linearly scales the carbon dioxide reduction activity. This study deciphers the complex structural response of the EDL and highlights its catalytic importance, which bridges the mechanistic gap between the EDL structure and electrocatalysis.

Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics

The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of samples which is required for estimations with high confidence increases disproportionally to obtain a reliable estimation of low-probability events. As a consequence, the Monte Carlo simulation becomes a non-realizable task from a computational perspective. We show that the application of machine learning algorithms significantly lowers the computational burden of the Monte Carlo method. We use artificial neural networks to predict structural response behavior using supervised learning. However, one shortcoming of supervised learning is the inability of a sufficiently accurate prediction when extrapolating to data the neural network has not seen yet. In this paper, neural networks predict the response of structures subjected to non-stationary ground excitations. In doing so, we propose a novel selection process for the training data to provide the required samples to reliably predict rare events. We, finally, prove that the new strategy results in a significant improvement of the prediction of the response statistics in the tail end of the distribution.

Correction: Park, S.; Jung, K. Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment. ISPRS Int. J. Geo-Inf. 2021, 10, 574

Missing Acknowledgments [...]

Full-scale Test on Emergency Repair Pavement under Airplane Loading

In order to reveal structural response law of emergency repair pavement under the airplane loading and verify the backfill material and structural applicability, two craters (Crater 1 composed of 2.4 m thick flying objects (FO) + 0.4 m thick graded crushed rocks (GCR) + 0.2 m thick roller compacted concrete + fibre reinforced plastic (FRP) course, and Crater 2 composed of 2.4 m thick FO + 0.6 m thick GCR + FRP course) were backfilled. Static and dynamic loads were applied using two airplanes. Results show that, laying FRP pavement layers reduced the maximum deflection of Crater 2 by 21%. Crater 1 and concrete pavement were both slightly rigid structures with a strong load transfer ability. The dynamic deflection basin curves of Crater 2 could be fit using a Gaussian function; while the curves of Crater 1 and concrete pavement could be fit using a quartic polynomial. Under static loading, the earth pressures of Crater 2 at −0.6 m, −0.4 m, and −0.2 m sites were 4.3, 9, and 9.6 times of those of Crater 1, respectively. At the −0.2 m site, the earth pressure of Crater 1 was 0.11 MPa, while that of Crater 2 reached 1.06 MPa. The research results can guide the rapid quality inspection and optimization design of emergency repair pavement structure and material.

Energy Absorption Enhancement by Unit Cell Angle Grading or Sandwich Panels with Auxetic Core

Architectured structures, particularly auxetic materials, have demonstrated encouraging applications in energy absorption as they facilitate the customization of their structural response. Specific geometries of unit cells can thus be tailored for particular needs due to recent progress in additive manufacturing techniques. This paper experimentally studies how the grading of the cell unit angle of an auxetic core in a sandwich panel affects its energy absorbing capability and structural response. 3D printed sandwich panels with uniform and graded auxetic cellular core were tested under quasistatic compression. The results show that sandwich panels with graded core exhibit much better energy absorption capabilities with higher plateau stress and densification strain. This indicates that, by appropriately controlling its geometry, auxetic structures can show further potential as core in sandwich panels for energy absorption applications.