Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.     

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.     

Assessment of early-age cracking of high-performance concrete in restrained ring specimens

High-performance concrete(HPC)is stronger and more durable than conventional concrete.However,shrinkage and shrinkage cracking are common phenomena in HPC,especially early-age cracking.This study assessed early-age cracking of HPC for two mixtures using restrained ring tests.The two mixtures were produced with water/binder mass ratio(mW/mB)of 0.22 and 0.40,respectively.The results show that,with greater steel thickness,the higher degree of restraint resulted in a higher interface pressure and earlier cracking.With steel thickness of 6 mm,19 mm,and 30 mm,the age of cracking were,respectively,12 days,8 days,and 5.4 days with the mW/mB=0.22 mixture;and 22.5 days,12.6 days,and 7.1 days with the mW/mB= 0.40 mixture.Cases of the same steel thickness show that the ring specimens with a thicker concrete wall crack later.With the mW/mB = 0.22 mixture,concrete walls with thicknesses of 37.5 mm,75 mm,and 112.5 mm cracked at 3.4 days,8.0 days,and 9.8 days,respectively;with the mW/mB= 0.40 mixture,the ages of cracking were 7.1 days,12.6 days,and 16.0 days,respectively.     

非球面超精密抛光技术研究现状

非球面应用范围的拓展以及应用精度要求的不断提高对非球面元件的尺寸精度与表面质量提出了更高要求;作为终加工手段,非球面超精密抛光技术也越来越受到世界各国的重视。掌握非球面超精密抛光过程中的材料去除机理以及由此导致的亚表面损伤等特性对提高非球面抛光的精度与效率十分重要。对非球面抛光技术的发展进行回顾,并根据非球面超精密抛光技术的发展脉络,阐述当前主要非球面超精密抛光技术的加工原理及加工实例,随后从亚表面损伤、边缘效应等方面对几种非球面超精密抛光技术进行比较,并以提高抛光精度与效率为目标,对非球面超精密抛光技术的发展趋势进行预测。     

Storage Changes in Anthocyanin Content of Red Raspberry Juice Concentrate

Anthocyanins from red raspberry juice concentrates were extracted with acidified ethanol and methanol and separated by high performance liquid chromatography. Six anthocyanin peaks were identified as cyanidin-3-sophoroside (cy-3-sop), cyanidin-3-glucorutinoside (cy-3-glru), cyanidin-3-glucoside (cy-3-gl), cyanidin-3-rutinoside (cy-3-ru), pelargonidin-3-sophoroside (pg-3-sop), and pelargonidin-3-glu-corutinoside (pg-3-glru). Relative proportions of individual anthocyanins were determined by comparing peak areas. The proportions of cy-3-sop, cy-3-glru and cy-3-gl in particular changed greatly on storage for 3 mo at + 20°C. Minor changes occurred when the raspberry juice concentrate was stored at ?20°C.     

CLASSIFICATION OF ON-LINE POULTRY CARCASSES WITH BACKPROPAGATION NEURAL NETWORKS1

A transportable system equipped with an overhead shackle conveying line and a visible/near-infrared (Vis/NIR) spectrophotometer system was assembled and used at a poultry slaughter plant. The reflectance spectra of each poultry carcass hung on the moving shackle was measured with a stationary fiber optic probe, which was set 2 to 5 cm away from the carcass, depending on the size. Reflectance spectra of wholesome and unwholesome poultry carcasses on the moving shackle, set at 60 or 90 birds/min, were measured, either under room light or in a dark environment. The scanning time for each carcass was 0.32 s. Most of the unwholesome poultry carcasses for this study were septicemic and air-sacculitic. The average accuracy in classifying wholesome and unwholesome carcasses was above 94%. All the misclassified carcasses were air-sacculitic. With a shackle speed of 90 birds/min, the highest average accuracy was obtained when the reflectance was measured in the dark (97.5%). The results showed that the accuracy of classification could be improved with the maintenance of a consistent lighting environment. All results indicated the Vis/NIR spectrophotometer system would be a highly accurate, robust tool for on-line, real-time classification of wholesome and unwholesome carcasses.     

Ex vivo characterization of human atherosclerotic iliac plaque components using cryo‐imaging

We characterized atherosclerotic plaque components with a novel cryo‐imaging system in lieu of standard histological methods commonly used for imaging validation and research endpoints. We aim to accurately identify plaque tissue types from fresh cadaver specimens rapidly (less than 5 h) in three dimensions for large specimens (up to 4 cm vessel segments). A single‐blind validation study was designed to determine sensitivity, specificity and inter‐rater agreement (Fleiss' Kappa) of cryo‐imaging tissue types with histology as the gold standard. Six naïve human raters identified 344 tissue type samples in 36 cryo‐image sets after being trained. Tissue type sensitivities are as follows: greater than 90% for adventitia, media‐related, smooth muscle cell ingrowth, external elastic lamina, internal elastic lamina, fibrosis, dense calcification and haemorrhage; greater than 80% for lipid and light calcification; and greater than 50% for cholesterol clefts. Specificities were greater than 95% for all tissue types. The results demonstrate convincingly that cryo‐imaging can be used to accurately identify most tissue types. If the cryo‐imaging data are entered into visualization software, three‐dimensional renderings of the plaque can be generated to visualize and quantify plaque components.     

Low complexity probability-based piecewise linear approximation of the sigmoid function

In order to improve the network recognition accuracy in the low complexity condition, a piecewise linear sigmoid function approximation based on the distribution probability of the neurons’ values is proposed only with one addition circuit. The sigmoid function is first divided into three fixed regions. Second, according to the neurons’ values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. The slope of the piecewise linear function is set as 2-n, effectively reducing the hardware implementation complexity. Experiments performed on Xilinx’s FPGA-XC7A200T implement the MNIST handwritten digits recognition. The results show that the proposed method achieves a 97.45% recognition accuracy in a deep neural network and 98.42% in a convolutional neural network, up to 0.84% and 0.57% higher than other approximation methods only with one addition circuit.     

Reliability of PMMA bone cement fixation: fracture and fatigue crack-growth behaviour

Fracture mechanics tests were performed to characterize the fracture toughness and fatigue crack-growth behaviour of polymethylmethacrylate (PMMA) bone cement, commonly used in joint replacement surgery. Compact tension specimens of various thicknesses were prepared and tested in both air and Ringers solution. Contrary to previous reports citing toughness as a single valued parameter, the PMMA was found to exhibit resistance-curve behaviour with a plateau toughness of 0.6 MPa m1/2 in air, and 2.0 MPa m1/2 in Ringers solution. The increased toughness in Ringers solution is thought to arise from the plasticizing effect of the environment. Under cyclic loads, the material displayed true mechanical fatigue failure in both environments; fatigue crack-growth rates, da/dN, were measured over the range 10-10 to 10-6 m/cycle and found to display a power-law dependence on the stress intensity range, K. The cement was found to be more resistant to fatigue-crack propagation in Ringers solution than in air. Wear debris was observed on the fatigue fracture surfaces, particularly those produced in air. These findings and the validity of using a linear-elastic fracture mechanics approach for viscoelastic materials are discussed in the context of providing more reliable and fracture-resistant cemented joints.