ANALYZING THE EFFECT OF MICRO RUBBER, MICRO SIO2, AND NANO SIO2 IN MICROCRACKS IN SELF-CONSOLIDATING CONCRETE (SEM OBSERVATION)

The present study is an attempt to analyze the effect of micro rubber waste in self- consolidating concrete (SCC) and to compare the concrete containing SCC with conventional additives such as micro SiO2 and nano SiO2. The use of rubber waste can be substantially important from the environmental point of view. Hence, concrete specimens containing 1, 3 and 5% micro rubber waste were made. Moreover, specimens containing 1, 3 and 5% nano SiO2 and 4, 8 and 12% micro SiO2 were prepared to compare their behaviour and microstructure with each other and with the witness specimens. The effect of the other parameters such as the specimen age and the w/c ratio on the microstructure of concrete containing rubber waste was also studied. Thereafter, the specimens were imaged using a scanning electron microscope (SEM) to observe and compare the microcracks in the concrete and secondary electron beam (SE) was used to obtain their images. The results of the microstructural consideration of different specimens showed that 1% of micro rubber waste can improve the behaviour of self-consolidating concrete, but the concrete microstructure strength and quality decline with an increase in its amount

Neuro-fuzzy application for concrete strength prediction using combined non-destructive tests

The application of the neuro-fuzzy inference system to predict the compressive strength of concrete is presented in this study. The adaptive neuro-fuzzy inference system (ANFIS) is introduced for training and testing the data sets consisting of various parameters. To investigate the influence of various parameters which affect the compressive strength, 1551 data pairs are collected from the technical literature. These data sets cover early and late compressive strengths from 3 to 365 days and low and high strength in the range 6·3–107·7 MPa. To reflect the effects of other uncertain parameters and in situ conditions, the results of non-destructive tests (NDTs) such as ultrasonic pulse velocity (UPV) and rebound hammer test are also included as input parameters, in addition to mix proportion and curing histories. For the testing of trained ANFIS models, 20 cube specimens and 210 cylinders are prepared, and compressive test and NDTs are conducted. For the comparative study of the applicability of ANFIS models combined with NDT results, four ANFIS models are developed. Depending on whether the input parameters of ANFIS models include NDT results or not, these are distinguished from each other. Among the four models, the ‘ANFIS-UR' model having the parameters for both UPV and rebound hammer test results shows the best accuracy in the prediction of compressive strength

Evolution, Monitoring and Predicting Models of Rockburst: Precursor Information for Rock Failure

Load/unload response ratio predicting of rockburst; Three-dimensional reconstruction of fissured rock; Nonlinear dynamics evolution pattern of rock cracks; Bayesian model for predicting rockburs

Prediction of healing performance of autogenous healing concrete using machine learning

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R(2)) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R(2)(GSA-GBR) = 0.958) and stronger robustness (RMSE(GSA-GBR) = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

MLP ANN Condition Assessment Model of the Turbogenerator Shaft A6 HPP Đerdap 2

This paper describes a model for estimating the condition of the shafts of turbines of the current generator in Hydropower plant Đerdap 2. For this purpose, an integral diagnostic approach was used. Based on the diagnostics of the condition of the shaft and the estimated lifetime, a multi-layer perceptron (MLP) based artificial neural network (ANN) is built, which is able to estimate the remaining lifespan of the turbine shaft. The MLP ANN model has not been made in this way on turbogenerators of hydroelectric power plant Đerdap 2 until now. The significance of this approach is that experiment brings about topology of ML ANN (number of neurons and layers) which is optimal for this model, training and testing. Results obtained from the neural network can be further used for decision-making about the moment of diagnosis or maintenance actions, as well as reducing stagnation and production losses

Predicting the Uniaxial Compressive Strength of a Limestone Exposed to High Temperatures by Point Load and Leeb Rebound Hardness Testing

The effect of exposure to high temperature on rock strength is a topic of interest in many engineering fields. In general, rock strength is known to decrease as temperature increases. The most common test used to evaluate the rock strength is the uniaxial compressive strength test (UCS). It can only be carried out in laboratory and presents some limitations in terms of the number, type and preparation of the samples. Such constrains are more evident in case of rocks from historical monuments affected by a fire, where the availability of samples is limited. There are alternatives for an indirect determination of UCS, such as the point load test (PLT), or non-destructive tests such as the Schmidt’s hammer, that can also be performed in situ. The aims of this research are: (i) measuring the effect of high temperatures and cooling methods on the strength and hardness of a limestone named Pedra de Borriol widely used in several historic buildings on the E of Spain, and (ii) studying the possibility of indirectly obtaining UCS by means of PLT and Leeb hardness tests (LHT), using Equotip type D. Limestone samples were heated to 105 (standard conditions), 200, 300, 400, 500, 600, 700, 800 and 900 ºC and cooled slowly (in air) and quickly (immersed in water). After that, UCS, PLT and LHT tests were performed to evaluate the changes as temperature increases. Results show decreases over 90% in UCS, of between 50 and 70% in PLT index and smaller than 60% in LHT index. Insignificant differences between cooling methods were observed, although slowly cooled samples provide slightly higher values than quickly cooled ones. The results indicate that LHT can be used to indirectly estimate UCS, providing an acceptable prediction. Research on correlating strength parameters in rocks after thermally treated is still scarce. This research novelty provides correlations to predict UCS in historic buildings if affected by a fire, from PLT and non-destructive methods such as LHT whose determination is quicker and easier.The authors acknowledge the support by Canteras Bernad SL which has generously provided samples, and Department of Geotechnical and Geological Engineering of Universitat Politècnica de València and Department of Civil Engineering of Universidad de Alicante, for its continuous support. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection Cracking

Although several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking are not yet well understood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional (3D) geometry or subjected to a non-simple stress state. In this study, the neural networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths and pavement structures. The coefficient of determination (R2) of all the developed NN models except one was above 0.99. Owing to the rapid prediction of SIFs using developed NN models, the overall computer run time for a 20-year reflection cracking prediction of a typical overlay was significantly reduced