Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.     

Physics-informed few-shot learning for wind pressure prediction of low-rise buildings

This research proposes a physics-informed few-shot learning model to predict the wind pressures on full-scale specimens based on scaled wind tunnel experiments. Existing machine learning approaches in the wind engineering domain are incapable of accurately extrapolating the prediction from scaled data to full-scale data. The model presented in this research, on the other hand, is capable of extrapolating prediction from large-scale or small-scale models to full-scale measurements. The proposed ML model combines a few-shot learning model with the existing physical knowledges in the design standards related to the zonal information. This physical information helps in clustering the few-shot learning model and improves prediction performance. Using the proposed techniques, the scaling issue observed in wind tunnel tests can be partially resolved. A low mean-squared error, mean absolute error, and a high coefficient of determination were observed when predicting the mean and standard deviation wind pressure coefficients of the full-scale dataset. In addition, the benefit of incorporating physical knowledge is verified by comparing the results with a baseline few-shot learning model. This method is the first of its type as it is the first time to extrapolate in wind performance prediction by combining prior physical knowledge with a few-shot learning model in the field of wind engineering. With the benefit of the few-shot learning model, only a low-resolution of the measuring tap configuration is required, and the reliance on physical wind tunnel experiments can be reduced. The physics-informed few-shot learning model is an efficient, robust, and accurate alternate solution to predicting wind pressures on full-scale structures based on various modeled scale experiments.     

Uncertainty propagation in puff-based dispersion models using polynomial chaos

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.     

Identification of relationship between sunspots and natural runoff in the Yellow River based on discrete wavelet analysis

Annual natural runoff is an important index of a river, which may be affected by solar activities. In this study, 304 years of annual natural runoff at the Sanmenxia station located in the Yellow River and the sunspot relative number are decomposed with the application of a Complex Morlet. According to the results of real part, modulus and second power of modulus, the annual runoff series at the Sanmenxia station has an obvious periodic oscillation on 90–100, 50–80, 35–50, 15–35, about 10, and less than 10-year scales. Also, there are obvious periodic variability with 60–90 years, 30–50 years and about 10 years. There are two centers of energy: one is about 1840–1850 on 7–11-year scale and the other is about 1825–1925 on 60–70-year scale. From the wavelet variance, 3, 26, 46, 68 year periods are detected within a 100-year scale, and the 68-year period is the most significant. Similar analyses are conducted for the sunspot relative number within the same period 1700–2003. The sunspot series shows 11- and 60-year period variation, as well as eight energy centers. Then, the correlation analyses for 11- and 60-year serial scales are computed. From a long-term period (1700–2003) view, there is no notable correlation between the natural runoff and the sunspot relative number; however, it is evident that the correlations exist within a short-term period. The results also indicate that the relationships between solar activities and the natural runoff in the Yellow River are complicated.     

南水北调工程江苏境内泵站混凝士配合比试验

探讨了粉煤灰、高效减水剂及引气剂掺量对混凝土性能的影响,为南水北调东线工程泵站混凝土提供施工配合比。通过掺人粉煤灰、高效减水剂及引气剂,配制了不同强度等级的混凝土,研究了C20、C25、C30等3种不同强度等级混凝土力学性能、耐久性能及体积稳定性,并用扫描电镜法（SEM）表征混凝土界面过渡区。研究表明：掺入粉煤灰、高效减水剂及引气剂后,混凝土立方体抗压强度均达到设计指标;在较高水胶比条件下,混凝土抗压、抗拉强度、弹性模量、抗渗性能均随着粉煤灰掺量增加而略有提高,同时掺入粉煤灰改善了混凝土界面过渡区。粉煤灰、高效减水剂及引气剂掺量分别不超过胶凝材料用量的30％、1％、0．015％时,所配制的C20、C25、C30混凝土满足南水北调工程江苏境内泵站混凝土施工要求。     

高精度GPS实时变形监测系统应用研究

三差分量测算法是GPS测量技术中的一个新突破.介绍了一种采用三差分量测算法的高精度GPS测量系统,并通过精度验证试验和工程应用来验证该GPS测量系统实时测量精度、测量技术、测量数据传输和处理技术.试验和工程应用研究所取得的大量实测数据表明,采用三差分的GPS三维变形实时测量技术具备毫米级测量精度,测量简单,测量数据集中处理,监测效率高,具备远程数据采集、接收和传输功能;该高精度GPS测量系统是工程表面变形实时监测工作中的一种新技术,在工程表面变形监测中具有良好的应用前景.     

两区模型和变密度模型溶质运移试验对比分析

为验证两区模型和变密度模型描述海水入侵过程中溶质运移现象的可行性,先后采用内径为9和11 cm的土柱进行室内稳定流土柱试验,讨论两区模型相比普通CDE模型的优点,以及采用直径10 cm,高300 cm土柱进行溶质运移试验,并应用非均质两区模型和过渡带变密度模型对验证结果作分析比较.采用决定系数和均方误差作为模型验证评价指标,结果表明:两区模型较普通CDE模型能更准确地模拟D9和D11土柱中溶质运移的整个过程.在模型对比土柱试验中两区模型和变密度模型都能很准确地描述溶质运移,说明两区模型在海水入侵模拟试验中存在应用的可能性.在数值模拟过程中同时发现流速基本不变的情形下,随着尺度的增加,溶质运移过程中的弥散系数也增大,且产生尺度效应,故需进一步作室外参数修正.     

小型水库大坝安全分类初步分析

结合小型水库,对国内外的大坝安全分类进行了初步分析。认为按库容划分小型水库的标准单一,三类坝的安全分类难以反映小型水库的大坝风险,建议对大坝安全进行风险分类。     

级配对堆石料颗粒破碎及力学特性的影响

级配是影响堆石料颗粒破碎和力学特性的重要因素。为研究级配对堆石料强度和变形特性的影响,开展了3种堆石料的大型三轴排水剪切试验;同时为分析不同级配堆石料的颗粒破碎特性,对试验前后试样进行了颗分试验。试验结果表明,颗粒破碎率随应力的增大而增大,在同一围压情况下,相比于细颗粒含量高的堆石料,细颗粒含量少的产生的颗粒破碎率要大。随细颗粒含量的增加,强度指标变大,抵御变形能力增强,剪切过程中产生的体积变形小,剪胀更为明显。