Evaluation and prediction of earth pressure balance shield performance in complex rock strata:A case study in Dalian,China

This research explores the potential for the evaluation and prediction of earth pressure balance shield performance based on a gray system model.The research focuses on a shield tunnel excavated for Metro Line 2 in Dalian,China.Due to the large error between the initial geological exploration data and real strata,the project construction is extremely difficult.In view of the current situation regarding the project,a quantitative method for evaluating the tunneling efficiency was proposed using cutterhead rotation(R),advance speed(S),total thrust(F)and torque(T).A total of 80 datasets with three input parameters and one output variable(F or T)were collected from this project,and a prediction framework based gray system model was established.Based on the prediction model,five prediction schemes were set up.Through error analysis,the optimal prediction scheme was obtained from the five schemes.The parametric investigation performed indicates that the relationships between F and the three input variables in the gray system model harmonize with the theoretical explanation.The case shows that the shield tunneling performance and efficiency are improved by the tunneling parameter prediction model based on the gray system model.

An integrated framework for automatic green building evaluation:A case study of China

With the burgeoning emphasis on sustainable construction practices in China,the demand for green building assessment has significantly escalated.The overall evaluation process comprises two key components:The acquisition of evaluation data and the evaluation of green scores,both of which entail considerable time and effort.Previous research predominantly concentrated on automating the latter process,often neglecting the exploration of automating the former in accordance with the Chinese green building assessment system.Furthermore,there is a pressing requirement for more streamlined management of structured standard knowledge to facilitate broader dissemination.In response to these challenges,this paper presents a conceptual framework that integrates building information modeling,ontology,and web map services to augment the efficiency of the overall evaluation process and the management of standard knowledge.More specifically,in accordance with the Assessment Standard for Green Building(GB/T 50378-2019)in China,this study innovatively employs visual programming software,Dynamo in Autodesk Revit,and the application programming interface of web map services to expedite the acquisition of essential architectural data and geographic information for green building assessment.Subsequently,ontology technology is harnessed to visualize the management of standard knowledge related to green building assessment and to enable the derivation of green scores through logical reasoning.Ultimately,a residential building is employed as a case study to validate the theoretical and technical feasibility of the developed automated evaluation conceptual framework for green buildings.The research findings hold valuable utility in providing a self-assessment method for applicants in the field.

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

Advancements and challenges in enhancing salt hydrate phase change materials for building energy storage:Optimization methodologies and mechanisms

The application of phase change materials(PCMs)into buildings is a prospective method for mitigating energy consumption in the construction sector.Among the diverse PCM options,salt hydrate PCMs stand out for their superior thermal storage densities,adaptable operating temperature ranges,and cost-effectiveness,rendering them highly attractive for practical engineering applications.However,the utilization of salt hydrates has encountered obstacles,including pronounced supercooling,severe phase separation,and insufficient thermal conductivity,limiting their efficacy in energy storage solutions.In response to these challenges and in pursuit of rendering salt hydrates viable for building energy storage systems,substantial research has been conducted in recent years.This paper offers a comprehensive overview of the strategies devised to address the challenges associated with salt hydrate PCMs,and it also elucidates the corresponding optimization methodologies and bolstering mechanisms,providing a valuable resource for researchers in this field.

Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts:Progress and perspectives

The increasing availability of building operational data has greatly encouraged the development of advanced data-driven technologies for smart building operations.Building operational data typically suffer from data quality problems,such as insufficient labeled and imbalanced data,making them incompatible with conventional machine learning algorithms.Recent advances in data science have provided novel machine learning paradigms to tackle such data challenges for practical applications,such as transfer learning,semi-supervised learning,and generative learning.This review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks,i.e.,building energy predictions,fault detection and diagnosis,and control optimizations.In-depth discussions have been provided to demonstrate the pros and cons of different learning approaches in terms of data compatibility,modeling difficulties,and possible application scenarios,which could be helpful for enhancing the feasibility of data-driven technologies for smart building operations.

Theoretical prediction of ground settlements due to shield tunneling in multi-layered soils considering process parameters

This paper conducts a theoretical analysis of ground settlements due to shield tunneling in multi-layered soils which are usually encountered in urban areas.The proposed theoretical solution which is based on the general form of the Mindlin’s solution and Loganathan-Poulos formula can comprehensively consider the in-process tunneling parameters including:unbalanced face pressure,shield-soil friction,unbalanced tail grouting pressure,unbalanced secondary grouting pressure,overloading during tunneling and the ground volume loss.The method is verified by comparing with the field data from the Qinghuayuan Tunnel Project in terms of the ground surface settlements along the longitudinal and transverse direction.Due to the local settlement or heave caused by the certain tunneling parameters,the ground surface settlements calculated using current solution along the longitudinal direction presents an irregular S-shaped curve instead of the traditional S-shaped curve.Results also find that the effect of the unbalanced secondary grouting pressure and the overloading during tunneling cannot be ignored.

Incorporating mitigation strategies in machine learning for landslide susceptibility prediction

This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)techniques.ML models,such as random forest(RF),logistic regression(LR),and support vector classification(SVC)are incorporated into GIS to predict landslide susceptibilities in Hong Kong.To consider the effect of mitigation strategies on landslide susceptibility,non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets.Two scenarios were created to compare and demonstrate the efficiency of the proposed approach;Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control.The largest landslide susceptibilities are 0.967(from RF),followed by 0.936(from LR)and 0.902(from SVC)in Scenario II;in Scenario I,they are 0.986(from RF),0.955(from LR)and 0.947(from SVC).This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities.The comparison between the different ML models shows that RF performed better than LR and SVC,and provides the best prediction of the spatial distribution of landslide susceptibilities.

A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

Short-term building energy predictions serve as one of the fundamental tasks in building operation management.While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven models.One promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive modeling.This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions.Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively.Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings.The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy predictions.The average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data.The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

Uncovering the relationship among spatial vitality,perception,and environment of urban underground space in the metro zone

With the rapid development of rail transit,effectively developing urban underground space(UUS)in the metro zone has become an important approach to expanding urban space.However,UUS is currently facing problems,such as an uneven distribution or even loss of vitality,which restricts the utilization efficiency of the space.Thus,we established a UUS environmental assessment system based on the‘‘comfort-aesthetic-function-traffic-structure”using space syntax,instrumental measurement,and questionnaire surveys.By constructing a partial-least-square structural equation model,the internal relationships between the UUS environment and corresponding space vitality and space perception under different study areas,namely the underground transportation-oriented space(transportation space for short)and the underground commercial-oriented space(commercial space for short),were studied in detail throughout the working day.Results indicate the following:(1)the UUS in the metro zone environment influences spatial vitality.The vitality distribution of transportation space is significantly affected by the spatial traffic and structure.The vitality distribution of commercial space is significantly affected by the spatial function and traffic.(2)The environment of UUSs in the metro zone influences users’s perception.The perception of transportation space is significantly affected by aesthetics and comfort.The perception of commercial space is significantly affected by aesthetic,comfort,and spatial functions.(3)The user’s perception affects vitality,and the effect is more significant in commercial space.This study provides an in-depth understanding of the relationship between the complex environment and its spatial vitality as well as the spatial perception of the UUS in metro zones.Our research results provide a novel approach and theoretical basis for the development and application of UUS vitality in various cities.

DEM analysis of passive arching in a shallow trapdoor under eccentric loading

This study analyzed the passive arching effect under eccentric loading by developing a series of trapdoor discrete numerical models.The numerical models were validated by comparison with laboratory test results.The deformation pattern,soil arching ratio,force chain distribution,and coordination number under various surcharge magnitudes and deviation distances were analyzed.The numerical results showed that the deformation diagram of soil particles can be divided into three zones:principal displacement zone,transition zone,and static zone.With an increase in the surcharge magnitude,the range of the principal displacement zone decreased,but the range of the transition region increased.The curve of the soil arching ratio on the trapdoor can be divided into three phases,which can be well characterized by the tangent modulus.The passive arching effect is degraded by a surcharge.The ulti-mate soil arching ratio could be approximated as a W-shaped distribution along the+x-direction.With an increase in the trapdoor displacement,the force chain on the trapdoor gradually expanded outward to form an inverted funnel shape.The most powerful force on the trapdoor was mainly distributed on its edge.The average coordination number decreased gradually as the trapdoor moved upward.