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High-rise residential building façades (HRBFs), given their size and abundant façade information, pose a challenge for conventional parsing methods. This paper presents FaçadeGraph, an approach for parsing the information of HRBFs into hierarchical attributed graphs. The method decomposes HRBF information into five hierarchical layers: ternary, floor, unit, space, and component. The façade elements are identified as semantics information with geometric attributes. The topological relationships between the elements are classified into affiliation, connection, aggregation, and decoration. The efficacy of FaçadeGraph was evaluated through the analysis of 36 HRBFs in China. The result showed that FaçadeGraph is effective in transforming diverse façade designs into consolidated graphs for automated syntax analyses. The paper contributes to the knowledge body of façade design by serving as an analytical tool for design feature analysis and underlying the development of generative HRBF design. • An attributed graph-based façade parsing approach is proposed. • A hierarchical structure is used to indicate façade elements for semantic analysis. • Universally applicable rules are defined for façade representation. • The framework is validated by a dataset of façades on 36 high-rise buildings. [ABSTRACT FROM AUTHOR]

The Aerial Building Machine (ABM) is a complex construction equipment used in high-rise building construction, facing challenges due to environmental uncertainties. This paper introduces a multi-condition generative design framework to improve ABM's visualization, operability, and intelligence. It seamlessly integrates real-time data between geometric and physical models and employs an ensemble deep learning model for objective value prediction, using a snapshot strategy. Combining structural reliability concepts with Latin hypercube sampling-based stochastic optimization, an optimal design scheme is obtained for uncertain loads. An ABM case study in China illustrates the approach's feasibility, showing it meets reliability requirements across different conditions and achieves significant improvements (16.59% under normal conditions and 16.91% under extreme wind conditions). Additionally, ensemble deep learning outperforms existing methods for ABM structural reliability estimation. Identifying optimal designs and evaluation options, this paper contributes a multi-condition optimization approach for enhanced structural reliability and establishes an efficient generative design workflow and system for exploring a vast solution space. • A multi-condition generative design framework for ABM is proposed. • The ensemble deep learning approach is employed to predict the structural response precisely. • The load uncertainties are considered according to the developed LHS-based SO approach. • A real aerial building machine in China is taken as a case study for illustration. • The proposed approach shows great performance in the structural optimization design. [ABSTRACT FROM AUTHOR]

In order to monitor the operating conditions of the construction industry, this paper incorporates the principal component analysis (PCA) and support vector machine (SVM) to predict the profitability of the construction companies listed on A-share market in China. With annual financial data in 2001–2012, this paper selected six indicators from different profitable perspectives to build a composite profitability index based on the PCA technique, and then established a SVM model to make the corporate profitability prediction of the construction companies in China. The results indicate that, the technical combination of the PCA and SVM can improve the profitability prediction significantly. In 2003–2012, the accuracy of predicting the profitability of the Chinese construction companies exceeded 80% on average. Compared with the artificial neural network (ANN), the SVM model has the superiority in the accuracy prediction of the Chinese construction companies. [ABSTRACT FROM AUTHOR]

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy ("FacalCE") is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and "FacalCE" contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds. • A deep learning method named DAPCNet is developed for 3D point cloud segmentation. • An improved 3D dual attention module is introduced to enhance model performance. • A custom loss "FacalCE" is designed to strengthen feature learning with handling data imbalance. • Conducting comparisons to examine the model performance for segmentation. • The developed method demonstrates outstanding performance, achieving an MIoU score of 0.8597 when applied to tunnel point clouds. [ABSTRACT FROM AUTHOR]

The construction sector in China released 5.08 billion tons of CO 2 in 2020, which accounted for 50.9% of national carbon emissions. With the establishment of a clean energy system, an increasing number of pumped storage hydropower (PSH) plants are scheduled to be constructed for energy storage. However, a gap remains in calculating carbon emissions for each specific building during design and later construction, considering the specific design parameters that affect these emissions. This paper provides a life cycle assessment (LCA) method for integrated carbon assessment for PSH design projects based on industry foundation classes (IFC), and the method is used to calculate the carbon emissions from each building directly at the design stage. PSH project carbon emissions are calculated by estimating the consumption of diesel, electricity and materials, together with the corresponding conversion factors. The proposed method is validated with data from real-world PSH projects and then used to analyze the carbon emissions of different buildings to determine the influence of various factors. This study could benefit future research related to the green evaluation of PSH design projects by improving carbon emission estimation based on IFC. • A LCA carbon assessment mathematical model in PSH with concrete face rockfill dams is proposed. • An extended IFC based semantic LCA model for carbon emissions assessment is discussed. • An IFC-based LCA prototype system is developed based on ifcOWL ontologies and rule engine. • The feasibility of IFC-enabled LCA for carbon assessment is validated via a case study. [ABSTRACT FROM AUTHOR]

Abstract: Cost estimation for tendering of building projects (TBP cost estimation, hereafter) is one of the most critical tasks in the architecture, engineering, construction, and facilities management (AEC/FM) industry. However, it is currently both time-consuming and prone to error because of the working complexity and comprehending deviation caused by the traditional two-dimensional representation of design. The advent of Building Information Modeling (BIM) technology enables a potential solution to these problems through the support of open data exchange standards such as the Industry Foundation Classes (IFC) standard. This paper discusses the key issues for semi-automatic and specification-compliant TBP cost estimation based on the IFC data of the design model, taking TBP cost estimation for the architectural and structural engineering of the cast-in-place concrete structure projects as an example. A Chinese national mandatory specification for TBP cost estimation is introduced as a representative specification and is interpreted into a computer-processable format. An IFC-based construction product information model of TBP cost estimation is established. A general process map and the involved key algorithms for semi-automatic and specification-compliant TBP cost estimation based on the IFC data of the design model are formulated. The application of the process map and algorithms in developing a prototype BIM-based software application for TBP cost estimation and the results of applying the prototype in an actual building project in China are presented. This paper lays a sound foundation for developing BIM-based software applications for TBP cost estimation in China, and demonstrates a feasible approach to satisfying the similar requirements in different countries. [Copyright &y& Elsevier]

The building and construction industry is the major industry of Hong Kong as in many developed countries around the world. After the commissioning of a high-rise building or a large estate, substantial manpower, both inside the management centre under a standby manner, as well as surveillance for security purposes around the whole building, is required for daily operation to ensure a quality environment for the occupants. If the surveillance job can be done by robots, the efficiency can be highly enhanced, resulting in a great saving of manpower and the improved safety of the management staff as a by-product. Furthermore, if the robot can retrieve commands from the building management system via a local area network (LAN), further savings in manpower can be achieved in terms of first-line fault attendance by human management staff. This paper describes the development of a robot prototype here in Hong Kong, which can handle some daily routine maintenance works and surveillance responsibilities. The hardware structure of the robot and its on-board devices are described. Real-time images captured by a camera on the robot with pan/tilt/zoom functions can be transmitted back to the central management office via a local area network. The interface between the robot and the building automation system (BAS) of the building is discussed. This is the first key achievement of this project with a strong implication on reducing the number of human staff to manage a modern building. Teleoperation of the robot via the Internet or intranet is also possible, which is the second achievement of this project. Finally, the robot can identify its physical position inside the building by a landmark recognition method based on standard CAD drawings, which is the third achievement of this project. The main goal of this paper is not the description of some groundbreaking technology in robotic development. It is mainly intended to convince building designers and managers to incorporate robotic systems when they are managing modern buildings to save manpower and improve efficiency. [Copyright &y& Elsevier]

This paper presents a hybrid machine learning model for predicting the pullback force in horizontal directional drilling (HDD) construction. The model combines the nondominated sorting genetic algorithm II (NSGA-II) and support vector machine (SVM). NSGA-II is used to optimize two hyperparameters in SVM. Different from other optimization algorithms, NSGA-II is a multi-objective optimizer, which considers prediction accuracy and stability. The proposed model is applied to two practical HDD projects in China. The prediction result is compared with the actual monitoring data, which shows that the mean absolute percentage errors (MAPE) are less than 7%. The primary conclusions are as follows: (1) The proposed model's accuracy and stability are better than those of the two benchmark models; (2) Machine learning models can predict the pullback force more accurately than the empirical model in the construction phase, and the maximum MAPE does not exceed 17%; (3) The running time of the proposed model is short, and it is feasible in practical application. Moreover, this paper discusses the practical application of machine learning models in HDD construction and the future development direction. • A hybrid machine learning model is proposed to predict pullback force during HDD construction. • Prediction accuracy and stability are considered. • Proposed model is applied to two HDD projects in China. [ABSTRACT FROM AUTHOR]

Laser scanning is used as a modern means to capture data from tunnels to assess their condition, but automated processing requires robust component detection and deterioration characterization. In order to segment 3D tunnel point clouds aiming at more accurate results with high time efficiency, this paper describes a point cloud technique that collects actual tunnel scenes and develops an attention-enhanced sampling point cloud network named ASPCNet. In the developed model, the feature embedding module is responsible to process the point cloud data for local features followed by the attention module for enhancing the feature extraction and learning. Additionally, the point downsampling-upsampling structure fully assists the model to strengthen the capability to process point clouds for time efficiency. In the training process, a weighted focal loss is designed to enhance the model learning by eliminating the effect of data imbalance. The developed ASPCNet is trained and then tested on a dataset collected from a cross-river metro tunnel section in China, demonstrating its efficiency and effectiveness. In comparison with different sampling ratios, state-of-the-art methods, and sampling methods, the ASPCNet with a uniform sampling rate of 2 exhibits the best performance, achieving an overall accuracy of 0.9758, a mean Intersection over Union (MIoU) of 0.8988, and an inference time of 4.1 s, demonstrating that the sampling structure involved in this research boosts the time efficiency, the developed model has superior performance, and the sampling method adopted is beneficial to strengthen the model performance. • A deep learning method named ASPCNet is developed for 3D point cloud segmentation. • It consists of several modules to process point clouds for accuracy and time efficiency. • A focal loss is designed to handle data imbalance and boost feature learning. • Effectiveness and efficiency are verified on a dataset with six classes of tunnel point clouds. • The developed method performs excellently with high accuracy and great efficiency. [ABSTRACT FROM AUTHOR]

In the desert region of northwest China, the frequency of wind-sand disasters is high. All types of concrete buildings built in this area face severe wind erosion due to high wind speed, resulting in varying degrees of wind-erosion damage to concrete. To accomplish intelligent identification of concrete wind-erosion damage, a concrete wind erosion experiment was conducted in the laboratory, and a concrete wind-erosion damage dataset was generated under the interference of water stains, scratches, shooting distance, and background noise. This paper combined with transformer theory to improve YOLO-v4 and proposed an object detection algorithm called MHSA-YOLOv4 suitable for wind-erosion damage of concrete. The results demonstrate that MHSA-YOLOv4 exhibits improved object detection performance than YOLO-v3, improved YOLO-v3, and YOLO-v4. On the test set, ACC, Precision, Recall, and mAP of MHSA-YOLOv4 are 91.30%, 91.52%, 92.31%, and 0.89, respectively. MHSA-YOLOv4 can accurately identify wind-erosion damage of concrete images under different test conditions, which reflects strong robustness. The applicability of computer vision technology to the intelligent identification of wind-erosion damage on concrete has been verified. • The dataset of wind-erosion of concrete was established to simulate the spalling of concrete. • A object detection algorithm called MHSA-YOLOv4 was proposed based on Transformer. • MHSA-YOLOv4 can detect concrete wind-erosion damage with high-precision. [ABSTRACT FROM AUTHOR]

Traditional construction progress tracking relies on labor-intensive activities with time lags, potential man-made errors, and inefficient progress management, which demands for an innovative and automated progress tracking approach. This paper describes a deep learning method that utilizes image segmentation to automatically evaluate the wall construction progress of an entire floor with the progress results streamlined to BIM. The approach was applied to a case study in China for assessing plastering construction activities with high segmentation accuracy (mean average precision = 96.8%). Further improvement of Mask Region-Based Convolutional Neural Networks (Mask R-CNN) and evaluation of its superiority over other models have also been discussed. This study provides both theoretical and practical references for unmanned supervision of progress tracking and intelligent schedule management. • Described an evaluation framework to recognize wall progress of an entire floor. • Proposed an image segmentation method to calculate wall construction area. • Improved Mask R-CNN algorithm to increase segmentation accuracy. • Discussed the influence of image light on segmentation accuracy. • Applied the framework to a case study with mean Average Precision of 96.8%. [ABSTRACT FROM AUTHOR]

Shield machine is an essential device for underground space development, and unmanned intelligent control of self-driving shields is productive research in tunnel construction technology. This paper analyzes the control functions of shield machines and proposes the definition and grading of self-driving shields. Inspired by the theory of human performance models, a Shield Automatic Intelligent Control System (SHIELD_AICS) is designed based on the combination of a data-driven and a knowledge-driven approach. A traditional shield with manual control was installed with SHIELD_AICS and transformed into an intelligent self-driving shield named Zhiyu shield. The Zhiyu shield was applied to two tunnel construction projects in China and completed the first autonomous unmanned tunneling successfully in the world. The field operation test shows that the self-driving shield is capable of automatic continuous tunneling for a long distance without manual intervention, and the shield attitude and the ground settlement level can meet the engineering requirements during tunneling. • A Shield Automatic Intelligent Control System (SHIELD_AICS) is established for the self-driving shield. • Automatic grading system of self-driving shield is defined and classified. • An approach based on knowledge and data-driven is proposed to realize control exploration, identification, tuning, and evaluation. • A traditional shield is transformed into a self-driving shield (Zhiyu) by hardware and software based on SHIELD_AICS. • Zhiyu shield completed the first unmanned tunneling, which has better decision-making and control performance than manual. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

Abstract: In order to analyze the dynamic evolutionary process of Chinese emerging housing market, the paper defines the behavioral functions of household agents in terms of demand generation, information search and price negotiation, and incorporates multi-agent technique and geographic information system to conduct a series of simulation studies on Beijing housing market. By setting different market scenarios, the impacts of new housing supply and information level on Beijing housing market are addressed. The findings show that, Beijing housing market is characterized by the prominent urban expansion with the formation of consolidated residential zones outside the 5th ring road; the growth in new housing supply can reduce the buyer search duration significantly, and lead the buyer search flow to the new housing market; higher information level can increase the sales of resale housing market, but inhibit the sales and time on market for new-built houses to some extent. [Copyright &y& Elsevier]

Abstract: At present, underground urban metro construction accidents in China are rising with the rapid growth of urbanization and infrastructure investment. Real-time safety and risk management during urban metro construction has become extremely important but is very difficult, time-consuming and unreliable due to the lack of information and experienced managers. This paper presents the development and application of a web-based system for safety risk early warning in urban metro construction. A hybrid data fusion model based on multisource information (monitoring measurements, calculated predictions, and visual inspections) is employed to imitate human experts to give safety risk assessment and early warnings automatically. In addition, it has significantly improved information collection, sharing and communication by establishing a collaborative platform instead of traditional manual management. The system has been successfully applied to several metro construction projects and has perfected the safety management performance in the cities of Wuhan, Shenyang, Zhengzhou and Kunming in China. [Copyright &y& Elsevier]

Abstract: Accurate sensors are the prerequisite for any building control and management operation to succeed. However, due to the lack of awareness of the importance, sensors are often out of calibration. This paper proposes a preventive approach to determine sensor maintenance requirements, which aims to explicitly quantify the importance of sensors and determine a proper calibration frequency for sensors with high importance. The approach takes account of the sensor bias growth mechanism and relates the sensor importance to a user-defined building performance index. The sensor importance is evaluated under a stochastic framework, which essentially is the way in which sensor bias grows. The calibration frequency is identified using a polynomial function which describes the degradation rate of the building performance index. The proposed approach is applied to a building located in Hong Kong. Results show that the proposed approach can effectively identify important sensors and their calibration frequency. [Copyright &y& Elsevier]

Abstract: Construction is undoubtedly the most dangerous industry in Hong Kong, being responsible for 76% of all fatal accidents in the region—around twenty times more than any other industry—and involving a loss of an estimated 145,000 man-days each year through accidents on site. In this paper, a new safety assessment method, termed the 4D Interactive Safety Assessment, is described which offers an improvement. This involves individual construction workers being presented with 4D virtual risky scenarios concerning their project and a range of possible actions for selection. The method provides an analysis of results which include the assessment of the correctness or otherwise of the user''s selections to contribute to an iterative process of retraining and testing until a satisfactory level of knowledge and skill is achieved. [Copyright &y& Elsevier]

Abstract: Proper design, reliable control and health of variable–air–volume (VAV) terminals are essential for reliable operation and energy efficiency of VAV air-conditioning systems. This paper presents an online fault diagnosis tool for pressure-independent VAV terminals and its implementation. The fault diagnosis tool is developed using a robust fault detection and diagnosis strategy for VAV terminals. Cumulative sum (CUSUM) control chart with estimated process parameters is utilized to detect faults in VAV terminals. A rule-based fault classifier is designed and utilized to find the sources of faults in VAV terminals. The fault diagnosis tool relies only upon the sensor data and control signals that are commonly available in building management control systems (BMCS). The fault diagnosis tool can be installed in BMCS to online read the operating data of all VAV terminals in a building through local area network and diagnose faults of VAV terminals in the building. The fault diagnosis tool has been validated in a large-scale office building located in Hong Kong and can effectively find all faults occurring in VAV terminals. [Copyright &y& Elsevier]

Abstract: This paper aims to discuss innovative plan models for Chinese cities that are currently under rapid development. The study considers Complexity Theories of Cities as the theoretical basis and applies a holistic approach in city planning by recognizing the complex nature of city. It strives to integrate the diverse local structure in social and spatial aspects with the ambition and demand of city''s expansion. Digital sequence simulation is used as an innovative tool to represent local activities, promote interventions and predict possible self-organization process in the future. The study arrives at an open scenario, the feasible prospect. A conclusion is drawn to reflect the process and achievement and propose future works for this research. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to investigate the possibilities and methods of applying the IFC (Industry Foundation Classes) standard, i.e. the mainstream standard of BIM (Building Information Modeling) data to the construction cost estimating for tendering in China. Through analyzing the existing standards and the IFC standard, the problems of using the IFC standard are identified and the solutions are explored. An information requirement model for construction cost estimating for tendering in China was established, which includes seven aspects of information entities. Then each aspect of the information was expressed by using the IFC standard to verify the completeness of the IFC standard and to establish the IFC-based information model. As a conclusion, the IFC standard can be used to express the information for the construction cost estimating for tendering in China as a whole, but some extensions in the form of proxy elements and property sets are needed. The established information model can be applied to the development of a construction cost estimating software. [Copyright &y& Elsevier]

Abstract: Earlier research works on PPP showed that an objective, reliable, and practical risk assessment model for PPP projects is essential to the successful implementation of PPP projects. However, actual empirical research studies in this research area are rather limited. This paper reports the second stage of a funded research study, which aims to develop a fuzzy synthetic evaluation model for assessing the risk level of a particular critical risk group (CRG) and the overall risk level associated with PPP projects in China. At the first research stage, thirty-four risk factors were identified through a comprehensive literature review and 3 new risk factors were proposed during a two-round Delphi questionnaire survey. The most critical 17 risk factors were selected through the calculation of normalized values. The correlation of these 17 critical risk factors (CRFs) was further analyzed via factor analysis and 6 CRGs were formulated, namely: (1) Macroeconomic Risk; (2) Construction and Operation Risk; (3) Government Maturity Risk; (4) Market Environment Risk; (5) Economic Viability Risk; and (6) Government Intervention. On the basis of the research works conducted at the first research stage, the weightings for each of the 17 critical risk factors (CRFs) and 6 CRGs were determined through the two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the 17 CRFs and 6 CRGs. The empirical research findings showed that the overall risk level of PPP highway projects is between “moderate risk” and “high risk”. Hence it could be construed that investment in PPP highway projects in China may be considered as risky. In fact, the Delphi survey respondents perceived that “Government Intervention” is the most CRG; with “Government Maturity Risk” being the second; “Economic Viability Risk” the third; “Market Environment Risk” the fourth; “Construction and Operation Risk” the fifth; and “Macroeconomic Risk” the last. These findings revealed that government intervention and corruption may be the major hurdles to the success of PPP highway projects in China. These may be caused by inadequate law and supervision system and poor public decision-making process. Although the fuzzy synthetic evaluation model was primarily developed for PPP projects in general, the research method could be replicated in a specific type of PPP project, such as water treatment projects and hospital projects, to produce similar models for inter-type comparisons. By doing so, it provides an opportunity for practitioners to assess the risk level of different types of PPP projects based on objective evidence rather than subjective judgment. The most CRG for different types of PPP projects could be identified and both precautionary and remedial actions could be taken as soon as possible. Such an extension would provide a deeper understanding of managing different types of PPP projects. [Copyright &y& Elsevier]

Abstract: Research into partnering performance measures for building and construction projects becomes crucial because a growing trend of client organizations has been observed to adopt partnering approach to procure their projects worldwide over the past decade. Although there are some related research studies and papers documented on this research area, few, if any, comprehensive and systematic research studies focus on developing a comprehensive, objective, reliable and practical performance evaluation model for partnering projects in construction. A Partnering Performance Index (PPI), which is composed of seven weighted Key Performance Indicators (KPIs), has been developed to measure, monitor, improve, and benchmark the partnering performance of construction projects in Hong Kong. A set of Quantitative Indicators (QIs) and well-defined ranges of Quantitative Requirements (QRs) for each QI have been further established using the Delphi survey technique and Fuzzy Set Theory. Evaluation of partnering success can now be based on quantitative evidences, thus tackling the subjectivity of performance evaluation. By making use of the Internet and database technology, PPI can be monitored on-line, thus saving much time, cost and efforts on data collection and retrieval than if they are done manually. As such, an Internet-based computerized partnering monitoring and benchmarking tool, namely the Computerized Partnering Performance Index System (CPPIS) has been developed. The Internet-based CPPIS enables project participants to input data at any time and location and the project administrators could perform data analysis via the Internet. The CPPIS also enables project managers to measure, monitor, improve and benchmark their partnering performances against those already stored in the database. Graphical presentations of data and various performance measures are also built in to assist various end-users to identify problematic areas and critical success factors for achieving partnering excellence. [Copyright &y& Elsevier]

Abstract: An ideal multi-objective financial decision support (MFDS) model for contractors should have ability of handling both the quantitative and qualitative inputs. This paper aims to develop a new MFDS model, which attempts to provide a more accurate way to support financial decision-makers in Chinese state-owned construction firms. Owing to the slow progress of passing the construction regulations, coupled with the inherited ownership problems in the Chinese booming construction industry, it is logical for Chinese project managers to make financial decisions with the consideration of the four objective functions (profit margin, risk factors, government relationship and market share), which are realistic representations of the present situation of Chinese construction industry. The proposed MFDS model is a practical method to support financial decision-making. For the model''s computational performance, Module 3 (stochastic universal sampling selection, adaptive crossover and adaptive mutation of Genetic Algorithm) is more superior. [Copyright &y& Elsevier]

Abstract: Prefabricated building components have been adopted in Hong Kong for over two decades for high-rise buildings. In the public sector, prefabrication together with standard modular design was introduced in the Housing Authority''s public housing projects in the mid-1980s. Over the years, precasting techniques have significantly evolved in public housing projects. In contrast, prefabrication has only recently been adopted in the private sector. The aim of this paper is to examine the evolution of precasting technology in high-rise residential developments in Hong Kong, and then explore the technological influences in both sectors. A database of 179 prefabricated residential buildings was developed and detailed case studies of five residential developments were conducted. The findings revealed that a greater extent of prefabrication use over the years, in terms of precasting percentage by volume and types of precast elements utilised. Major prefabrication innovations, in both sectors, influenced the technological advancement in prefabrication in Hong Kong. [Copyright &y& Elsevier]

The construction industry is experiencing remarkable growth in the data generation. Data mining (DM) from considerable amount of data in the construction industry has emerged as an important tool for knowledge discovery. Despite the remarkable growth of DM applications to the construction industry, a systematic review on DM applications in this field is still lacking. Therefore, this paper attempts to provide a comprehensive literature review of DM application articles published between 2001 and 2019 with the specificity of construction industry. The popularity of DM applications in the construction industry is increasing, especially after 2016, with a plurality emanating from China. The main data sources, DM functions, and frequently used DM techniques in the construction industry are discussed in detail. Nine major application fields are identified, with the primary research interests focusing on multiple dimensions of energy, safety management, building occupancy and occupant behavior, material performance, and textual knowledge discovery. Four major challenges and four future research directions are proposed by drawing on the research findings. This study provides academics and practitioners with a more comprehensive understanding of the state-of-the-art of DM applications and heuristic implications for future studies. • A comprehensive review of data mining (DM) application in construction industry from 2001 to March 2019 is conducted. • The popularity of DM applications in construction industry is increasing, especially after 2016. • 9 major application fields are identified and comprehensively analyzed. • Four challenges and four future directions are proposed. [ABSTRACT FROM AUTHOR]

The construction of undersea tunnels is an extremely risky endeavor that is vulnerable to water seepage and gushing due to the high water pressure, complex geological conditions, and pore water trapped in unstable rocks. This risk can lead to the collapse of tunnels under construction and disastrous consequences of fatalities and injuries as well as project delays and financial losses. The current risk management practices for tunnel construction projects in China are static and rely on the subjective judgement of experts and practitioners and do not incorporate real-time monitoring data during the construction process at this time. This paper presents a new method and system to assess and manage the risks during the construction process by coupling the risk management system and the quality management system and integrating jobsite monitoring data, design data, and environmental data. In this new method and system, the risk factors are categorized into (hu)man, material, machine, method, and environment, or 4M1E, and are quantitatively measured. The Dempster-Shaffer (D-S) theory was adopted in this method to both fuse the 4M1E data and to compute the aggregate risk index. This new method and system was tested during the Xiamen Metro Line No. 3 project when a shield machine cutter accident occurred. The results show that, before the accident, the individual risk measures in all five dimensions (4M1E) and the aggregate risk index were extremely high, which clearly illustrated the feasibility and capability of the newly developed method and system. • Risk management system and quality management system are coupled for quantitative risk ranking. • Jobsite monitoring data, design data, and contextual data are integrated for risk assessment. • Risk factors are categorized into (hu)man, material, machine, method, and environment, or 4M1E. • The combination of D-S evidence theory and fuzzy method for fusing heterogeneous data. • Validation through a real shield machine accident in a cross-sea tunnel project. [ABSTRACT FROM AUTHOR]

Establishing the balance between slurry supporting pressure and expected water-earth pressure is an important criterion to ensure excavating face stability in shield tunneling. To overcome the inaccuracy and hysteresis of manual operations, this paper presents a model predictive control (MPC) system for the slurry pressure balance during construction through effectively regulating the slurry circulation and air pressure holding systems according to geological conditions. The MPC structure consists of a diagonal recurrent neural network (DRNN) that approximates the complex relationship between slurry pressure and tunneling parameters, an optimizer which produces the optimal air pressure and slurry level based on the multi-step ahead predictions, and an evolved particle swarm optimization (EPSO) algorithm. The proposed EPSO can update the structure and weights of DRNN concurrently to better cater to the changeable stratum. The optimizer can excellently compensate the time delays in slurry pressure regulation by incorporating the logical control sequence of actuator systems into the EPSO procedure. The simulation results demonstrated that the presented approach can accurately track the desired water-earth pressure and significantly enhance the robustness of slurry supporting system in tunneling, and the novel EPSO also performed higher convergence speed and precision than the classic algorithms used for comparison. A multi-step predictive control system for slurry pressure balance is proposed to ensure excavating face stability in shield tunneling, which can also overcome the inaccuracy and hysteresis of manual operations effectively. Unlabelled Image • A MPC system for slurry pressure balance in shield tunneling is proposed. • The input features of DRNN for slurry pressure prediction are analyzed. • Periodic supervision and mutation based PSO is developed to train DRNN. • A multi-step predictive controller and involved anti-delay PSO are established. • The performance of MPC system is verified in Wangjing railway tunnel in China. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]