A Super-Resolution Reconstruction Driven Helmet Detection Workflow

The degrading of input images due to the engineering environment decreases the performance of helmet detection models so as to prevent their application in practice. To overcome this problem, we propose an end-to-end helmet monitoring system, which implements a super-resolution (SR) reconstruction driven helmet detection workflow to detect helmets for monitoring tasks. The monitoring system consists of two modules, the super-resolution reconstruction module and the detection module. The former implements the SR algorithm to produce high-resolution images, the latter performs the helmet detection. Validations are performed on both a public dataset as well as the realistic dataset obtained from a practical construction site. The results show that the proposed system achieves a promising performance and surpasses the competing methods. It will be a promising tool for construction monitoring and is easy to be extended to corresponding tasks.

A Prediction Method for the RUL of Equipment for Missing Data

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

The Effects of Precrack Angle on the Strength and Failure Characteristics of Sandstone under Uniaxial Compression

Characterization of the mechanical properties of cracked rock masses is essential for ensuring the long-term stability of the engineering environment. This paper is aimed at studying the relationship between the strength characteristics of specimen and the angle of precrack, as well as the interaction of cracks under uniaxial compression. To this end, two sandstone specimens, distinguished with a single and three precracks, were built using the PFC software. For the former case, both the peak strength and elastic modulus increase to a peak value as the crack angle α gets closer to the forcing (loading) direction. For the latter case, the strength experiences a trend of increasing-maintaining trend as the crack angle α gets closer to the forcing direction, and the elastic moduli are barely affected. For the specimens containing a single precrack, their crack numbers increased approximately in a one-step or two-step stair pattern with increasing axial strain; whereas for the specimens containing three cracks, their crack numbers all showed a multistep growth trend. Furthermore, the failure mode of the specimen is closely related to the precrack angle. However, if the precrack distribution does not affect the original crack propagation path, it will hardly affect the mechanical properties of the specimen.

Study on Deterioration Characteristics of Low-Carbon Steel’s Mechanical Properties and Fracture Mechanism under Marine Engineering Environment

In order to study the mechanical properties of low-carbon steel under the coupling effect of the overall environment and the loads, the tensile mechanical test was carried out. The results indicated that, as the sea water concentration and tensile deterioration increased, both the mass-loss rate and surface roughness of the low-carbon steel gradually increased, and the yield strength, tensile strength, elongation, and section shrinkage decreased gradually. The mechanical parameters of the low-carbon steel were affected by the joint actions of the sea water concentration and tensile deterioration. The established mechanical model of low-carbon steel under the marine engineering environment shows that tensile deterioration had no effects on the fracture toughness, while the increase of sea water concentration could reduce the fracture toughness remarkably.

Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum–Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data

Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum–Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum–Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution.

Failure Analysis of Railroad Components

Because of the tough engineering environment of the railroad industry, fatigue is a primary mode of failure. The increased competitiveness in the industry has led to increased loads, reducing the safety factor with respect to fatigue life. Therefore, the existence of corrosion pitting and manufacturing defects has become more important. This article presents case histories that are intended as an overview of the unique types of failures encountered in the freight railroad industry. The discussion covers failures of axle journals, bearings, wheels, couplers, rails and rail welds, and track equipment.

Summary of Slope Ecological Protection Technology

It is an important issue for sustainable economic development to give consideration to both project development and environmental protection. At present, we must confront and seriously deal with the problems of rational utilization of resources, protection of the environment and beautification of the environment in engineering construction. This paper describes a variety of slope plant protection technology, which can not only play a good role in slope protection, but also improve the engineering environment and reflect the beauty of natural environment, so as to provide reference for slope ecological protection.