Estimation of tunnel axial orientation in the interlayered rock mass using a comprehensive algorithm

The axial selection of tunnels constructed in the interlayered soft-hard rock mass affects the stability and safety during construction.Previous optimization is primarily based on experience or comparison and selection of alternative values under specific geological conditions.In this work,an intelligent optimization framework has been proposed by combining numerical analysis,machine learning(ML)and optimization algorithm.An automatic and intelligent numerical analysis process was proposed and coded to reduce redundant manual intervention.The conventional optimization algorithm was developed from two aspects and applied to the hyperparameters estimation of the support vector machine(SVM)model and the axial orientation optimization of the tunnel.Finally,the comprehensive framework was applied to a numerical case study,and the results were compared with those of other studies.The results of this study indicate that the determination coefficients between the predicted and the numerical stability evaluation indices(STIs)on the training and testing datasets are 0.998 and 0.997,respectively.For a given geological condition,the STI that changes with the axial orientation shows the trend of first decreasing and then increasing,and the optimal tunnel axial orientation is estimated to be 87.This method provides an alternative and quick approach to the overall design of the tunnels.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Identifying the validity domain of machine learning models in building energy systems

The building sector significantly contributes to climate change.To improve its carbon footprint,applications like model predictive control and predictive maintenance rely on system models.However,the high modeling effort hinders practical application.Machine learning models can significantly reduce this modeling effort.To ensure a machine learning model’s reliability in all operating states,it is essential to know its validity domain.Operating states outside the validity domain might lead to extrapolation,resulting in unpredictable behavior.This paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about it.For that,a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning model.Suitable novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates.A subset of five algorithms is then evaluated on building energy systems.First,on two-dimensional data,displaying the results with a novel visualization scheme.Then on more complex multi-dimensional use cases.The methodology performs well,and the validity domain could be approximated.The visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage:Case study of China’s 35 public buildings

For the significant energy consumption and environmental impact,it is crucial to identify the carbon emission characteristics of building foundations construction during the design phase.This study would like to establish a process-based carbon evaluating model,by adopting Building Information Modeling(BIM),and calculated the materialization-stage carbon emissions of building foundations without basement space in China,and identifying factors influencing the emissions through correlation analysis.These five factors include the building function type,building structure type,foundation area,foundation treatment method,and foundation depth.Additionally,this study develops several machine learning-based predictive models,including Decision Tree,Random Forest,XGBoost,and Neural Network.Among these models,XGBoost demonstrates a relatively higher degree of accuracy and minimal errors,can achieve the RMSE of 206.62 and R2 of 0.88 based on testing group feedback.The study reveals a substantial variability carbon emissions per building’s floor area of foundations,ranging from 100 to 2000 kgCO_(2)e/m^(2),demonstrating the potential for optimizing carbon emissions during the design phase of buildings.Besides,materials contribute significantly to total carbon emissions,accounting for 78%e97%,suggesting a significant opportunity for using BIM technology in the design phase to optimize carbon reduction efforts.

Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings.The integrated model consists of five base models and a meta-model,which significantly improves the prediction performance.Specifically,the R2 value was improved by 9.19% and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47%,79.88%,67.32%,and 57.02%,respectively,compared to the single prediction model.According to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance.In the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the building.Specifically,the optimal design solution reduces the energy use intensity by 31.6 kWh/m^(2),improves the useful daylight index by 39%,and modulated the thermal comfort index,resulting in a decrement of 0.69℃ for the summer season and an enhancement of 0.64℃ for the winter season,respectively.Overall,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.

Leveraging machine learning to generate a unified and complete building height dataset for Germany

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

Tall Buildings with Dynamic Facade Under Winds

Burgeoning growth of tall buildings in urban areas around the world is placing new demands on their performance under winds.This involves selection of the building form that minimizes wind loads and structural topologies that efficiently transfer loads.Current practice is to search for optimal shapes,but this limits buildings with static or fixed form.Aerodynamic shape tailoring that consists of modifying the external form of the building has shown great promise in reducing wind loads and associated structural motions as reflected in the design of Taipei 101 and Burj Khalifa.In these buildings,corner modifications of the cross-section and tapering along the height are introduced.An appealing alternative is to design a building that can adapt its form to the changing complex wind environment in urban areas with clusters of tall buildings,i.e.,by implementing a dynamic facade.To leap beyond the static shape optimization,autonomous dynamic morphing of the building shape is advanced in this study,which is implemented through a cyber–physical system that fuses together sensing,computing,actuating,and engineering informatics.This approach will permit a building to intelligently morph its profile to minimize the source of dynamic wind load excitation,and holds the promise of revolutionizing tall buildings from conventional static to dynamic facades by taking advantage of the burgeoning advances in computational design.

Synthesis,Structure Characterization and Photoluminescence of a Novel Complex with(6,4,4)-Network [Ce_2(2,5-pydc)_3(H_2O)_2](2,5-pydc = Pyridine-2,5-dicarboxylic Acid)

A new three-dimensional supramolecular [Ce2（2,5-pydc）3（H2O）2]（1） has been hydrothermally synthesized at 180 ℃ and characterized by single-crystal X-ray diffraction.X-ray crystal analyses reveal that the compound belongs to the monoclinic system,space group P21/c,C21H13Ce2N3O14,a = 6.561（1）,b = 17.986（5）,c = 9.411（3） ,β = 95.558（5）° and Z = 2.In the structure of 1,each Ce（1） center is surrounded by 2,5-pydc ligands,forming the 6-connected node,and the 2,5-pydc ligand coordinates to the Ce（Ⅲ） in two different coordination modes.In mode 1,the four oxygen atoms of two carboxyl groups connect neighboring Ce（Ⅲ） ions,giving 4-connected（4-c） second building unit（SBU-1）.Furthermore,the structure is extended into a 2-D layer from SBU-1 by sharing Ce（1） atoms.In mode 2,the ligand coordinates to the Ce（Ⅲ） ion from the adjacent chain with the 4-connected（4-c） second building unit（SBU-2）,generating a 1-D ladder from SBU-2 by sharing Ce（1） atoms.Finally,the structure is extended into a 6,4,4-c network.Its photoluminescence property was also investigated.

Challenges and opportunities of machine learning control in building operations

Machine learning control(MLC)is a highly flexible and adaptable method that enables the design,modeling,tuning,and maintenance of building controllers to be more accurate,automated,flexible,and adaptable.The research topic of MLC in building energy systems is developing rapidly,but to our knowledge,no review has been published that specifically and systematically focuses on MLC for building energy systems.This paper provides a systematic review of MLC in building energy systems.We review technical papers in two major categories of applications of machine learning in building control:(1)building system and component modeling for control,and(2)control process learning.We identify MLC topics that have been well-studied and those that need further research in the field of building operation control.We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

Ensemble machine learning framework for daylight modelling of various building layouts

The application of machine learning(ML)modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment.Although many advancements have been made,no standardized ML modelling framework exists in daylight assessment.In this study,625 different building layouts were generated to model useful daylight illuminance(UDI).Two state-of-the-art ML algorithms,eXtreme Gradient Boosting(XGBoost)and random forest(RF),were employed to analyze UDI in four categories:UDI-f(fell short),UDI-s(supplementary),UDI-a(autonomous),and UDI-e(exceeded).A feature(internal finish)was introduced to the framework to better reflect real-world representation.The results show that XGBoost models predict UDI with a maximum accuracy of R^(2)=0.992.Compared to RF,the XGBoost ML models can significantly reduce prediction errors.Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

Nondestructive testing algorithm of building concrete material defects based on machine learning

In order to pay more attention to the quality of construction concrete and accurately judge whether concrete material meets the standard,a nondestructive testing algorithm of building concrete material defects based on machine learning is proposed.Through the ray tracing algorithm of Snell’s theorem,the shortest path between two random punctuation marks of building concrete is calculated.The original coordinate system and grid size were set,the trend and length of the line in the grid were calculated,and the coordinates between the grid corner points and the transmitting probe were calculated so as to obtain the position of the intermediate refractive points of the two probes.Finally,the vector dot product of the local defects is obtained by the optimal hyperplane calculation of the binary classification in the support vector machine.Experimental results show that the proposed method has the advantages of high precision.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

党建引领下的高职育人队伍链路构建

立足于新时代中国特色社会主义教育背景,以党建为抓手,探析高职院校育人队伍链路,健全高职院校立德育人和思政育人机制,加大提升职业院校思政育人、立德树人的针对性、实效性和有效覆盖面,结合高职院校的实际情况,该文从宿舍建设、班级建设、团学干部建设入手,以发展入党积极分子为主,建立育人第一链路;将辅导员选聘和教师党员队伍建设结合,建立育人第二链路;通过实施全员协同育人、全过程贯通育人、全方位融通育人,由全体教职工组建育人全链路,有效形成党建引领下的高职育人队伍链路。

ENERGY ASSESSMENT OF URBAN BUILDINGS BASED ON GEOGRAPHIC INFORMATION SYSTEM

Urban building energy analysis has attracted more attention as the population living in cities increases as does the associated energy consumption in urban environments.This paper proposes a systematic bottom-up method to conduct energy analysis and assess energy saving potentials by combining dynamic engineering-based energy models,machine learning models,and global sensitivity analysis within the GIS(Geographic Information System)environment for large-scale urban buildings.This method includes five steps:database construction of building parameters,automation of creating building models at the GIS environment,construction of machine learning models for building energy assessment,sensitivity analysis for choosing energy saving measures,and GIS visual evaluation of energy saving schemes.Campus buildings in Tianjin(China)are used as a case study to demonstrate the application of the method proposed in this research.The results indicate that the method proposed here can provide reliable and fast analysis to evaluate the energy performance of urban buildings and determine effective energy saving measures to reduce energy consumption of urban buildings.Moreover,the GIS-based analysis is very useful to both create energy models of buildings and display energy analysis results for urban buildings.

以改进机器视觉算法构建纸张图像识别模型

为保障纸张生产加工质量,精准获取与识别纸张缺陷,以改进机器视觉算法构建了纸张图像识别模型。首先以由线阵CCD相机与双光源等构成的图像采集装备采集纸张缺陷图像,其次以改进机器视觉方法对纸张缺陷图像进行预处理分析,然后将预处理后图案以可变形卷积神经网络输入进行训练,以此检测识别纸张所存在的缺陷类型。实验测试结果表明,基于改进机器视觉算法的纸张图像识别模型可高效且精准识别缺陷,准确率高达98.4%,拥有较高识别度,可广泛推广以投入实际运用。

基于机器学习的水泥基灌浆料强度预测方法

针对采用小直径芯样法准确预测水泥基灌浆料抗压强度的问题,使用压力试验机分别对水泥基灌浆料标准尺寸试块和小直径芯样进行抗压强度试验,并基于试验数据,采用支持向量机回归(SVR)和随机森林回归(RFR)对水泥基灌浆料抗压强度进行回归预测。结果表明:标准尺寸试块均呈正反相接的四角锥体破坏形态,而高径比为0.7和1.0的小直径芯样呈正反相接的圆锥体破坏形态,高径比为1.2的小直径芯样呈斜裂缝剪切破坏形态;标准尺寸试块和小直径芯样的抗压强度值均服从正态分布,且无离群值;随着龄期的增长,标准尺寸试块和小直径芯样的抗压强度提高,且具有早期强度较高的特性;直径46 mm芯样的抗压强度较小,且更易受加工精度的影响;在给定的龄期和直径下,高径比为0.7的芯样抗压强度值最大,抗压强度离散程度最小;RFR预测模型对水泥基灌浆料抗压强度的预测效果更优。所提方法可较准确预测水泥基灌浆料抗压强度,为水泥基灌浆料抗压强度的预测研究提供了参考。

基于RF-SFLA-SVM的装配式建筑高空作业工人不安全行为预警

为有效预警装配式建筑高空作业工人不安全行为的发生趋势或状态,增强对装配式建筑工人不安全行为(PBWUBs)的管控,采用随机森林(RF)-混合蛙跳算法(SFLA)-支持向量机(SVM)模型,开展工人不安全行为预警研究。首先,采用SHEL模型分析处于高空作业危险中的PBWUBs的影响因素,并通过RF确定关键预警指标;然后,采用SFLA对SVM的参数进行寻优改进;最后,利用RF-SFLA-SVM预警高空作业PBWUBs,提出应对措施,并与其他预警模型对比。研究结果表明:基于RF-SFLA-SVM预警高空作业PBWUBs,准确率最高,为91.67%,与其他模型的预警性能相比,最高提升14%。研究结果可为高空作业PBWUBs的防控提供参考。

基于群控的板换在通信机楼节能的应用研究

随着能源短缺问题日益凸显,节能减排成为了当今社会的重要议题。机楼作为能源消耗的大户,其节能优化尤为重要。本文深入探究基于群控的板式换热器在机楼节能和安全方面的应用,结合实际案例,通过研究群控技术与板换技术理论分析和实证研究,分析群控系统对板换的自动控制,探讨其节能效果和实际应用价值。

中国机械制造业现代学徒制实施现状分析及策略

现代学徒制是职业院校为适应中国制造2025战略,培养先进制造领域高素质高技能人才的行之有效且符合职业教育规律的重要人才培养模式。文章从机械制造类现代学徒制培养模式、核心元素的利益诉求、培育标准、课程开发与实现机制、考核评价机制、沟通平台建设六个维度进行深入分析并提出实施策略,以期为进一步推进现代学徒制工作提供启示和借鉴。

习近平文化思想的丰富内涵、辩证品格和重大意义

新时代宣传思想文化工作之所以取得历史性成就,最根本的原因就在于有习近平新时代中国特色社会主义思想特别是其文化篇的科学指引。习近平文化思想是一个不断展开的、开放的思想体系,内涵丰富深刻,通过实现“第二个结合”推进又一次思想解放,不断巩固中华文化主体性,推动建设中华民族现代文明的新时代文化,确保国家文化安全并牢牢掌握党对意识形态工作的领导权,以弘扬人类共同价值促进文明交流互鉴。这一思想具有辩证品格和鲜明特色,突出地体现在辩证地认识和处理魂脉和根脉、人民性和党性、传承性和创新性、民族性和世界性、理论和实践、危和机等中国式现代化文化建设中的一系列重大关系。这一思想具有深远历史意义、重大现实意义和持久未来意义。