Digital Image Correlation for Evaluation of Cracks in Reinforced Concrete Bridge Slabs

In proof-loading of concrete slab bridges, advanced monitoring methods are required for identification of stop criteria. In this study, Two-Dimensional Digital Image Correlation (2D DIC) is investigated as one of the governing measurement methods for crack detection and evaluation. The investigations are deemed to provide valuable information about DIC capabilities under different environmental conditions and to evaluate the capabilities in relation to stop criterion verifications. Three Overturned T-beam (OT) Reinforced Concrete (RC) slabs are used for the assessment. Of these, two are in situ strips (0.55 × 3.6 × 9.0 m) cut from a full-scale OT-slab bridge with a span of 9 m and one is a downscaled slab tested under laboratory conditions (0.37 × 1.7 × 8.4 m). The 2D DIC results includes full-field plots, investigation of the time of crack detection and monitoring of crack widths. Grey-level transformation was used for the in situ tests to ensure sufficient readability and results comparable to the laboratory test. Crack initiation for the laboratory test (with speckle pattern) and in situ tests (plain concrete surface) were detected at intervals of approximately 0.1 mm to 0.3 mm and 0.2 mm to 0.3 mm, respectively. Consequently, the paper evaluates a more qualitative approach to DIC test results, where crack indications and crack detection can be used as a stop criterion. It was furthermore identified that crack initiation was reached at high load levels, implying the importance of a target load.

Diagrammatic Iconicity in EkeGusii: A relation between the structure of form and meaning

This paper mainly presents evidence for a relationship between language structure and meaning in EkeGusii, a Bantu language spoken in Kenya. The main argument is that the structure of language mirrors the structure of reality. A brief overview of other scholars demonstrates that diagrammatic iconicity shows universal tendencies. Five main ideas run down the discussion. Firstly, in EkeGusii, speakers sub-consciously cluster sounds around related meanings, evidencing gestalt and relative iconicity. Secondly, there is evidence of overlap of morphological and phonetic iconicity, an aspect of phonaesthesia. Thirdly, reduplication in certain infinitives demonstrates the reality of phono-iconicity in EkeGusii, augmented by unpleasant sound sequences. Fourthly, certain onomatopes in EkeGusii are actually diagrammatic, indicating that there is no one stop criterion for classifying overlapping types of icons. And finally, the paper posits that iconicity intersects with arbitrariness showing that language has both motivated and discrete symbols.

Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This paper introduces a mutation only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art non-linear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.

Agent-based modelling of interstellar contacts using rumour spread models

Some stochastic model of rumours asserts that even an advanced communication network does not guarantee every agent hears certain news because they predict that rumour spreaders convert to stifflers when contacted with an informed agent. In this study, we adapted two rumour spread models to interstellar communication by developing an agent-based model (ABM) for exploring the issue more rigorously. We enhanced the spread models by adding two additional parameters called conversion probability and stop-criterion, which represent the eagerness and persistency of civilizations to establish new contacts. Results of the ABM under several settings suggest that limited SETI searches lead to undiscovered civilizations. Earth may be one of these undiscovered civilizations although an advanced communication network might already be set up. Hence, we speculate that rumour spread models can propose another solution to Fermi's Paradox.

Concepção de um sistema de automação e monitoramento para equipamento utilizado em ensaios de trilhamento elétrico e erosão

<p>This paper aimed at the design and development of a data acquisition and control system using the Arduino open-source platform to automate equipment responsible for the IEC 60587 electrical tracking and erosion test. The developed system allows the selection of protection resistors specified by the standard from the voltage value informed by the operator, monitoring of the leakage current flowing over five samples simultaneously tested and automatically interrupts the samples if the leakage current exceeds 60 mA for more than two seconds. The leakage current values are measured indirectly from the voltage drop across 50 Ω shunt resistors installed in series with each sample. The voltage values on the shunt resistors are conditioned by a measuring circuit that allows the voltage level to be adjusted to the analog inputs of the microcontroller, ie, between 0 V and 5 V. The microcontroller treatment performs the voltage signal obtained by the measuring circuit, the calculation of the RMS value of the current and stop criterion monitoring the leakage current. The calibration of the leakage current measurement circuit was performed by comparing voltage values measured by a digital oscilloscope for four different alternate waveforms and values up to 5 Vrms, corresponding to currents up to 100 mA. The results showed that the circuit provided measurements close to the values measured by the oscilloscope, with errors below 11%. For current values between 30 mA and 80 mA, the errors were less than 6%.</p>

ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design

Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a comparison between the results of a regular optimization and the results obtained in the present study is presented.

An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.