Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss

Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time. The real-world multivariate time series comes with noises and contains complicated local and global temporal dynamics, making it difficult to forecast the future time series given the historical observations. This work proposes a simple and effective framework, coined as TimeSQL, which leverages multi-scale patching and smooth quadratic loss (SQL) to tackle the above challenges. The multi-scale patching transforms the time series into two-dimensional patches with different length scales, facilitating the perception of both locality and long-term correlations in time series. SQL is derived from the rational quadratic kernel and can dynamically adjust the gradients to avoid overfitting to the noises and outliers. Theoretical analysis demonstrates that, under mild conditions, the effect of the noises on the model with SQL is always smaller than that with MSE. Based on the two modules, TimeSQL achieves new state-of-the-art performance on the eight real-world benchmark datasets. Further ablation studies indicate that the key modules in TimeSQL could also enhance the results of other models for multivariate time series forecasting, standing as plug-and-play techniques

Effects of Hybrid PVA–Steel Fibers on the Mechanical Performance of High-Ductility Cementitious Composites

Producing high-ductility cementitious composites (HDCC) increased in parallel with concrete demand in China recently. However, the high cost of manufacturing cementitious composites (HDCC) persists. To reduce the cost of HDCC, steel fibers, polyvinyl alcohol (PVA), and river sand were used to produce HDCC concrete in the present study. A total fiber content of 2% was formed with five different proportions of PVA fiber and steel fiber. Within the scope of the experimental studies, mechanical (workability, compressive strength, tensile, and bending properties), and microstructural (scanning electron microscopy) tests were carried out to investigate the properties of the hybrid fiber-reinforced composites. The results showed that the fluidity of HDCC increased with increasing steel fiber substitution. The compressive strength of the mixture containing 0.5% steel fiber and 1.5% PVA fiber exhibited a better compressive strength of 31.3 MPa. The tensile performance of the mixture was improved due to the incorporation of steel fiber. The initial cracking strength was about 2.32 MPa, 25.4% higher than that of the reference group, and the ultimate tensile strength was 3.36–3.56 MPa. However, reducing the content of PVA fiber impacts the flexural rigidity of the matrix

The Influence of Excitation Method on the Strength of Glass Powder High-Strength Cementitious Materials

Recycling economy and the re-utilization of solid waste have become important parts of sustainable development strategy. To improve the utilization rate of waste glass, glass powder high-strength cementitious material (GHSC) was prepared by replacing part of the cement in the cementitious material with ground waste glass powder. Firstly, the effect of glass powder particle size on the flexural and compressive strength of GHSC was investigated by the gray correlation method, and the optimal grinding time was obtained. Additionally, the effect of the magnitude of steam curing temperature and the length of steam curing time on the compressive strength and flexural strength of GHSC was investigated, and the mechanism of the effect of the curing regime on the strength was explored by examination of the microstructure. Finally, to simplify the curing process of GHSC, the effects of Ca(OH)2 and Na2SO4 as excitation agents on the compressive strength and flexural strength of GHSC at different dosing levels were compared. The results showed that glass powder with a particle size of less than 20 μm would improve the compressive strength and flexural strength of the specimen. Steam curing can significantly improve the flexural strength and compressive strength of GHSC specimens. At a steam curing temperature of 90 °C for a duration of three days, the compressive strength and flexural strength of GHSC increased by 76.7% and 98.2%, respectively, compared with the standard curing specimens. Ca(OH)2 and Na2SO4 as excitation agents significantly enhanced the compressive and flexural strengths of GHSC under standard curing conditions

THE ENVIRONMENTAL CHALLENGE AND HEALTH SECURITY IN CHINA

China has achieved impressive rapid development over the past 30 years. But China also faces the challenge of environmental change resulting from rapid economic growth and the attendant risks to human health. In this paper we described the environmental change and health risk in China from evident fluctuation of China’s climate, major changes in natural hydrological condition, raw materials and energy demand, changes of disease epidemic pattern related to climate change and ecosystem damage, new health risk raised by rapid urbanization and rural environmental quality degradation. The suggestion and countermeasures were discussed

Effects of Hybrid PVA–Steel Fibers on the Mechanical Performance of High-Ductility Cementitious Composites

Producing high-ductility cementitious composites (HDCC) increased in parallel with concrete demand in China recently. However, the high cost of manufacturing cementitious composites (HDCC) persists. To reduce the cost of HDCC, steel fibers, polyvinyl alcohol (PVA), and river sand were used to produce HDCC concrete in the present study. A total fiber content of 2% was formed with five different proportions of PVA fiber and steel fiber. Within the scope of the experimental studies, mechanical (workability, compressive strength, tensile, and bending properties), and microstructural (scanning electron microscopy) tests were carried out to investigate the properties of the hybrid fiber-reinforced composites. The results showed that the fluidity of HDCC increased with increasing steel fiber substitution. The compressive strength of the mixture containing 0.5% steel fiber and 1.5% PVA fiber exhibited a better compressive strength of 31.3 MPa. The tensile performance of the mixture was improved due to the incorporation of steel fiber. The initial cracking strength was about 2.32 MPa, 25.4% higher than that of the reference group, and the ultimate tensile strength was 3.36–3.56 MPa. However, reducing the content of PVA fiber impacts the flexural rigidity of the matrix

Replacing Fly Ash or Silica Fume with Tuff Powder for Concrete Engineering in Plateau Areas: Hydration Mechanism and Feasibility Study

Abundant tuff mineral resources offer a promising solution to the shortage of fly ash (FA) and silica fume (SF) resources as emerging supplementary cementitious materials. However, a lack of clarity on its hydration mechanism has hindered its practical engineering application. In this study, high SiO2-content tuff powder (TP) was examined to assess the mechanical and workability performance of mortar specimens with varying particle sizes of the TP as complete replacements for FA or SF. Microscopic analysis techniques, including X-ray diffraction (XRD), differential thermal analysis (DTG), and energy-dispersive X-ray spectroscopy (EDS), were employed to elucidate the hydration mechanism of the TP and its feasibility as a substitute for SF or FA. Results indicated that TP primarily functions as nuclei and filler, promoting cement hydration, with smaller particle sizes amplifying the hydration ability and increasing Ca(OH)2 and C-S-H gel content. The specimens with TP (median particle size 7.58 μm) demonstrated 9.2% and 29.9% higher flexural and compressive strengths at 28 days, respectively, compared to the FA specimens of equal mass. However, fluidity decreased by 23.1% accordingly. Due to TP’s smaller specific surface area compared to SF, the TP specimens exhibited higher fluidity but with decreased strength relative to the SF specimens. Overall, TP shows potential as a replacement for FA with additional measures to ensure workability

Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries