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Additive manufacturing (AM) is a powerful and promising manufacturing technology. Advances in AM have led to increasing demands for its applications; however, controlling the geometry of deposited objects during the AM process remains a major challenge. Geometry control and compliance to a target geometry are some of the key concerns for AM, especially for a prototype requiring high precision in its geometry. In this study, an online geometry estimation methodology for continuous monitoring during the directed energy deposition (DED) process was developed using a laser line scanner. Our proposed methodology comprises (1) real-time scanning of each deposited track's profile, (2) online extraction of the track's geometry, and (3) online plotting and comparison of the as-designed and as-built models. In the methodology, data analysis following real-time scanning, such as geometry discrepancy estimation and online plotting of the as-built model, is attained for both single- and multi-layer objects. The effectiveness of our developed methodology is examined by comparing the profiles of the single- and multi-layer objects estimated during the DED process with the reference profiles obtained via laser line scanning and microscopy after the completion of the DED process.

Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does. To improve a building’s natural ventilation, it is essential to develop models to understand the relationship between wind flow characteristics and the building’s design. Significantly more effort is still needed for developing such reliable, accurate, and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics (CFD) simulation. This paper, therefore, presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings. The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building. Once the surface pressures have been obtained, multizone modelling is used to predict the air change per hour (ACH) for different flats in various configurations. Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error, mean absolute percentage error, and a fusion algorithm respectively. These data-driven models are used to predict the ACH of 25 flats. The results from multizone modelling and data-driven modelling are compared. The results imply a high accuracy of the data-driven prediction in comparison with physics-based models. The fusion algorithm-based neural network performs best, achieving 96% accuracy, which is the highest of all models tested. This study contributes a more efficient and robust method for predicting wind-induced natural ventilation. The findings describe the relationship between building design (e.g., plan layout), distribution of surface pressure, and the resulting ACH, which serve to improve the practical design of sustainable buildings.

In this work, we study higher $B_c$ mesons to the $L=S$, $P$, $D$, $F$, $G$ multiplets using the Cornell potential model, which takes account of the screening effect. The calculated mass spectra of $B_c$ states are in reasonable agreement with the present experimental data. Based on spectroscopy, partial widths of all allowed radiative transitions and strong decays of each state are also evaluated by applying our numerical wave functions. Comparing our results with the former results, we point out difference among various models and derive new conclusions obtained in this paper. Our theoretical results are valuable to search for more $B_c$ mesons in experiments., Comment: 18 pages

Tendering & Bidding generally involve many construction units participating in a project bidding. The purchaser selects the best bidder with a short construction period, low cost, high quality, and good reputation. There are huge economic benefits and points of concern behind it. Tendering suffers an increased risk of data tampering due to the centralized database architecture, resulting in huge financial losses and unnecessary consumption of human resources. Blockchain technology has gained increasing attention in the construction industry because of its high transparency, information traceability, non-repudiation, and non-tampering characteristics. Initial explorations have been carried out to discuss blockchain benefits in the e-tendering process. However, existing studies are limited to conducting qualitative analysis or proposing conceptual frameworks. A workable blockchain-based that supports basic functions in the e-tendering process still lacks. Therefore, as an initial exploration, this paper presents a secure blockchain-based framework for tender evaluation. Besides, technical components such as tendering smart contracts are developed to support framework functionalities. The framework feasibility is validated in a tendering evaluation example. Results show that the proposed framework guarantees secure tender evaluation by keeping data traceability and immutability.

We study the excited states of $ω$ and $ω_3$ by comparison with the $ρ$ and $ρ_3$ families, and discuss the possibility of $X(2220)$ as $ω$ excitation by analyzing the mass spectra and strong decay behaviors. In addition, we predict the masses and widths of $ω(2D)$ and $ω_3$and $ρ_3(4D)$, $ρ_3(1G)$, $ω_3$ and $ρ_3(2G)$ and $ω_3(3G)$ and $ρ_3(3G)$. The abundant information of their two-body strong decays predicted in this work will be helpful to further study of these $ω$ and $ω_3$ and $ρ$ and $ρ_3$ states in experiment and theory., 13 page,1 figure. PRDedition. arXiv admin note: text overlap with arXiv:1910.12408

Recently, the BESIII Collaboration reported three resonances: $X(2232)$ with $M = 2232 \pm 19 \pm 27$ MeV and $Γ= 93 \pm 53 \pm 20$ MeV, $X(2200)$ whose mass $M = 2200 \pm 11 \pm 17$ MeV and width $Γ= 74 \pm 20 \pm 24$ MeV as well as $X(2222)$ which has mass of $2222 \pm 7 \pm 2$ MeV and the width of $59 \pm 30 \pm 6$ MeV. The mass spectrum of $ω$ meson family is studied utilizing the modified Godfrey-Isgur model, and the two-body strong decays of $X(2232)$, $X(2200)$ and $X(2222)$ within two different approaches of the $^3P_0$ model. We find that the newly discovered states $X(2232)$, $X(2200)$ and $X(2222)$ may be the same and are most likely to be the $ω(3D)$ state. The discovery could be useful in establishing entire $ω$ mesons., 5 pages,2 figures

Evacuation navigation in emergencies such as fires is one of the most important operational considerations for a building. The large and complicated interior spaces, as well as the intensive popula...

Detailed planning, sequencing, and scheduling for the installation of multiple pipe systems are greatly needed to efficiently complete a piping project with both reduced cost and duration. ...

The building sector represents nearly one third of the global greenhouse gas (GHG) emissions, in which the embodied carbon of reinforced concrete is recognized as a significant source of the GHG emissions. Developing low carbon reinforced concrete buildings is an important strategy to achieve the long term sustainability in urban cities. However, the large variability and the combined effect of different reinforced concrete design parameters during the production stage presents considerable effects on the total embodied carbon of buildings, which were not fully explored in literature. Thus, this study aims to develop a systematic approach to quantify the detailed relationship between different concrete design parameters and their combined effects, thereby helping mitigate the building GHG emissions. The proposed approach takes into account the parameters that have significant effects on the embodied carbon of reinforced concrete, and studies different variations of reinforced concrete designs to minimize the carbon emissions. The results of this study serve as a decision support basis for improving the building design in regard to carbon reduction. In an illustrative example, the proposed approach is examined to compare the embodied carbon for different material design scenarios and to suggest the material choice for a lower embodied carbon in concrete buildings.

Semantic segmentation of closed‐circuit television (CCTV) images can facilitate automatic severity assessment of sewer pipe defects by assigning defect labels to each pixel on the image, f...

Rapid growth of building components in the BIM object database increases the difficulty of the efficient query of components that users require. Retrieval technology such as Autodesk Seek in America and BIMobject in Europe, which are widely used in BIM databases, are unable to understand what the search field truly means, causing a lack of completion and a low accuracy rate for results incapable of meeting the demands of users. To tackle such a problem, this paper puts forward a natural-language-based intelligent retrieval engine for the BIM object database and Revit modeling. First, a domain ontology is constructed for semantic understanding, and the BIM object database framework is established for testing our search engine. Second, “target keyword” and “restriction sequence” proposed are extracted from the natural sentences of users. Then, a final query is formed, combining concepts of “keyword” and “restriction sequence”, and its concepts are expanded through the semantic relationship in ontology. Finally, the results are presented after mapping from the final query to the BIM object database and ranking of results. Compared with traditional keyword-based methods, the experimental results demonstrate that our method outperforms the traditional methods.

Modular design refers to a design approach whereby customized modules or components are assembled to form the layout plan of a building. Previous researches have attempted to optimize the layout plan design of low-rise houses for maximizing the natural daylighting, ventilation performance, and energy efficiency. Engineers have also studied the modular design of high-rise residential buildings to meet site constraints and to optimize site development potentials. However, the previous studies on modular building design were based on empirical trial-and-error approaches, efficient methods for identifying the optimal combination of different modules and components were still lacking in literature. Therefore, this study attempts to develop an innovative approach for optimizing the modular design of high-rise residential buildings, with the aim of maximizing the building energy performance while fulfilling the site constraints and design code requirements. The design optimization problem, including the design variables and objective functions, is properly formulated to guarantee the quality of final optimized deign. Provided a set of well-defined modules and components, evolutionary genetic algorithm (GA) is then utilized for the wide-ranging exploration of the building layout plans, taking into consideration the site conditions and building design requirements. A computer program is developed, coupling the GA optimization and energy modeling, to systematically evaluate the candidate layout plans. The energy simulation results are subsequently used to guide the GA towards finding the optimal design solution. The proposed optimization method is utilized to generate the optimal layout design for a 40-story high-rise residential building, using a set of pre-defined modular flat units. The optimal design maximizes the use of natural ventilation and daylighting to save 30-40% of the energy consumption without compromising the site constraints and design requirements. The findings of this study serve as the decision support basis to enhance modular design of high-rise residential buildings (such as energy conservation in this study), thereby improving the sustainability and cost-effectiveness of the built environment.

Accurate estimation of object edge lines is the key for accurate dimension estimations. However, due to the limited spatial resolution of terrestrial laser scanning and the existence of mixed pixels along object edges, edge line estimation is always imperfect. Most of the existing edge line estimation algorithms simply remove mixed pixels before edge line estimation without utilizing mixed pixel data. This study developed high-accuracy edge line estimation algorithms for flat surfaces using 3D terrestrial laser scanning. In this study, four different edge line estimation algorithms are proposed and compared, and two of them leverage information from mixed pixels to improve the edge line estimation accuracy. The effects of scan data parameters on the performance of the edge line estimation algorithms are investigated using numerical simulations and validation experiments. Based on the simulation results, recommendations of edge line estimation algorithm and scanning settings are provided, which are further validated through experiments.

Maintenance workers on offshore platforms are usually exposed to a high level of noise from the working environment as most of the daily operations of oil and gas process machines generate noise over 85 dBA, causing substantial health and safety issues. Avoiding exposure of workers to the modules that generate high sound power during maintenance activities can significantly mitigate the noise impact on human health and safety. Noise simulation and noise mapping methodologies can be used to evaluate and quantify the noise impact on offshore platforms. However, limited digital information of offshore platforms makes noise simulation setup challenging as modules on topsides have a high level of details. In addition, current noise mapping studies are usually conducted in a 3D static manner, which only reflects noise impact at a certain time. Building information modeling (BIM) provides detailed physical and functional characteristics of a facility that can be applied to support the noise simulation on offshore platforms. In this study, attempts have been made to develop a BIM-supported 4D acoustics simulation approach to mitigating the noise impact on maintenance workers of offshore platforms. BIM is utilized to automatically provide required information to facilitate noise simulation setup. 4D acoustics simulation approach is used to obtain the spatio-temporary sound pressure level (SPL) distribution of the noise generated by the functional modules on offshore platforms. Acoustic diffusion equation (ADE) is selected as noise SPL prediction model. To evaluate noise impact on maintenance workers, an equation based on daily noise dose is then newly derived to quantify the noise impact. Optimization algorithm is used to determine the maintenance schedule with the minimum daily noise dose. Finally, optimized maintenance schedule that has considered noise impact is used to update the daily maintenance plan on offshore platforms. An example of a fixed offshore platform with maintenance daily activity information is used to illustrate the proposed BIM-supported 4D acoustics simulation approach. The results show that the developed approach can well mitigate noise impact on maintenance workers on offshore platforms, resulting in health and safety management improvement.

Buildings use significant amount of energy worldwide, with mechanical air-conditioning systems accounting for a large proportion of the total energy consumed. Utilizing natural ventilation can achieve a comfortable indoor thermal environment while reducing the use of air-conditioning and building energy consumption. However, previous studies mainly focused on either thermal comfort or energy conservation by utilizing natural ventilation. The correlation between thermal comfort and energy performance due to the effect of natural ventilation is important, but has not been studied thoroughly in literature. Moreover, natural ventilation in buildings is greatly affected by building geometry, material property, outdoor environmental condition and occupancy, which were not fully incorporated in the previous studies. This paper presents a framework based on building information modeling (BIM) to study the effect of natural ventilation on the correlation between thermal comfort and energy performance. BIM provides 3D building models with detailed geometric and material information as well as the building location and type that help determine the outdoor environmental condition and occupancy, thereby improving the accuracy of natural ventilation simulation. A case study was conducted using the proposed framework, aimed at investigating the natural ventilation in a residential flat in Hong Kong at different seasons. The results show that utilizing natural ventilation cannot always achieve thermal comfort. Natural ventilation brings cool air in late spring (April) during which the indoor temperature can be controlled at a comfortable level and the energy use for mechanical ventilation can be saved. The proposed framework helps evaluate the natural ventilation in buildings for maintaining indoor thermal comfort at minimal energy use, thereby creating a more sustainable built environment.

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.

As the coming decades will witness a big trend in the decommissioning of offshore platforms, simultaneously disassembling topsides of multiple offshore platforms is getting increasingly common. Considering high risk and cost of offshore operations, module lift planning among multiple offshore platforms with transport vessels is required to be carefully conducted. The lift planning usually contains two main parts: module layout on vessels planning and vessel transport schedules arrangement. In contrast to the current experience-driven module lift planning, this paper formulates the lift planning optimization problem and develops a web system integrating building information modeling (BIM) and geographical information system (GIS) to efficiently disassemble topsides for multiple offshore platforms. BIM provides detailed information required for planning module layout on vessels and GIS contains the management and analysis of geospatial information for the vessel transport schedule arrangement. As for module layout optimization, three heuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) are implemented and compared to obtain the module layout with the minimum total lift time. While for vessel transport schedule, graph search technique is integrated with a developed schedule clash detection function to obtain the transport schedule with the minimum sailing time. The proposed optimization algorithms and techniques are integrated into a developed BIM/GIS-based web system. An example of three offshore platforms with eighteen modules in total is used to illustrate the developed system. Results show that the developed system can significantly improve the efficiency of lift planning in multiple topsides disassembly. The developed BIM/GIS-based web system is also effective and practical in the resource allocation and task assignment among multiple locations, such as construction sites, buildings, and even cities.

Additional file 1.

When a Simple Harmonic Oscillator (SHO) wave function is used as an effective wave function, a very important parameter in the SHO wave function is the effective $β$ value. We obtain the analytical expression of $β_{eff}$ ($β_{effective}$) of the SHO wave function in coordinate space and momentum space. The expression is applied to the light meson system $(u\bar{u},~u\bar{s})$ to compare the behavior of $β_{eff}$. The results show that $β_ {eff,\mathbf{r}}$ in coordinate space and $β_ {eff,\mathbf{p}}$ in momentum space are significantly different in the ground state, however, similar in the highly excited states with Cornell potential., 10 pages,3 figure

Document management systems in AEC projects manage important project documents such as schedules, RFIs, and change orders. Hence, security concerns in document management systems especially involving data integrity of documents and records may have a severe effect on a project in terms of money and the reputations of project participants. Therefore, in this research blockchain technology is leveraged to facilitate data integrity in document management for construction applications through - (1) irreversible and irrevocable approval workflow logic via smart contract technology, (2) irreversible recording of document changes via blockchain ledger technology, and (3) document version history integrity via a blockchain-based data structure. A prototype of the proposed smart contract framework was developed using Hyperledger fabric and evaluated. The scalability of the proposed framework to support document version integrity was also evaluated and discussed. A formulation based on existing literature is developed to evaluate the cost viability of the proposed framework.

Mechanical, electrical and plumbing (MEP) system provides various services and creates comfortable environments to residents in cities. To enhance the management efficiency of the highly complex MEP system, as-built building information modeling (BIM) is increasingly adopted in the world. Currently, as-built BIMs are mostly drawn manually by modelers in BIM modeling software referring to point clouds or on-site photos, which is time consuming and labor intensive. This study presents a novel fused BIM reconstruction approach for MEP scenes. The proposed approach makes the best of the rich semantic information provided by images and accurate geometry information provided by 3D LiDAR point clouds. Firstly, a state-of-the-art deep learning model focusing on semantic segmentation is fine-tuned for the MEP dataset, and then RGB images collected with depth camera are segmented with the well-trained model. Secondly, taking the segmented images and the corresponding depth images as input, a semantic-rich 3D map is generated. Thirdly, an instance-aware component extraction algorithm in LiDAR point clouds given approximate object distribution in 3D space is developed. In the component extraction algorithm, a label transfer technique is proposed to firstly determine the rough locations of targeting objects in LiDAR point clouds. Then, accurate component locations are determined for three types of components including irregular shaped components, regular shaped components, and secondary components attached to walls. Finally, the BIM model is reconstructed based on component extraction results. To validate the proposed technique, experiments were conducted in four MEP rooms in a water treatment plant in Hong Kong. It is demonstrated that the proposed technique is more accurate and more efficient with wider range of applications compared to previous BIM reconstruction methods.

Background: A complex interplay between the different cell types presented in the epithelium leads to activation of the luminal acidifying capacity of the epididymis, a process that is crucial for sperm maturation and storage. Basal cells of epididymal epithelium sense the luminal angiotensin II (ANG II) and stimulate proton secretion in clear cells through nitric oxide (NO). Our previous study has shown the chemokine regulated upon activation normal T-cell expressed and secreted (RANTES) was expressed in the F4/80 positive cells of human epididymis. The objective of this study was to explore the involvement of RANTES in regulating the luminal acidification in the rat epididymis. Methods: The role of RANTES was explored by in vivo perfusion with recombinant RANTES, Met-RANTES, and PBS of different pH values. Furthermore, rat vasectomy was established to alter the epididymal luminal pH. RIA was used to measure the tissue homogenate ANG II concentration. Real time-PCR and western blot were used to detect the expression levels of AGTR2, RANTES, CCR1, CCR5 and iNOS in epididymis. Results: RANTES was restricted to the macrophages of epididymal ducts and co-localized with its receptors CCR1 and CCR5. Both V-ATPase and iNOS were up-regulated in the cauda epididymis after perfusing with recombinant RANTES, while the antagonist Met-RANTES perfusion led to a complete abrogation of the increased expression of V-ATPase on the apical membrane of clear cells and iNOS in macrophages. Upon alkaline perfusion, RANTES expression was significantly increased and the apical accumulation of V-ATPase on the clear cells was induced in the cauda epididymis. The luminal pH in the cauda epididymis increased after vasectomy. The concentration of the ANG II and the expression levels of AGTR2, RANTES, CCR1, CCR5 and iNOS were reduced in the cauda epididymis following vasectomy. Conclusion: Activation of AGTR2 in basal cells by luminal ANG II may stimulate RANTES in basal macrophages to induce the production of NO, which increases proton secretion by adjacent clear cells. Thus, RANTES is possible to participate in the crosstalk among basal cells, macrophages and clear cells for the fine control of an optimum acidic luminal environment that is critical for male fertility.

Augmented reality/virtual reality (AR/VR) has been increasingly adopted to enhance the visualization of building information modeling (BIM) models. However, there is a lack of mechanisms fo...

Energy efficiency is an important aspect in building design nowadays. During the design phase, energy simulation and analysis can enhance the overall building energy efficiency at a lower cost and less delay to the project schedule. Currently, energy analysis is performed digitally in energy simulation software applications which require users to draw a virtual model and define the parameters in the application platform before running the simulations. As building information modeling (BIM) technology becomes increasingly adopted in the architecture, engineering and construction/facility management (AEC/FM) industry, BIM models are often used in construction projects. However, these BIM models are not compatible with the existing energy simulation applications, and the required model conversion often results in information loss and distortion. One reason is that the current BIM data schemas, such as the Industry Foundation Classes (IFC), do not fully support data for energy simulation and analysis. This paper presents a data model to extend BIM schemas for supporting energy simulation and analysis. The data model was developed by studying the four most widely used energy simulation software, namely eQUEST and EnergyPlus. The proposed data model was then used to extend the latest IFC4 schema. Based on the results, a mapping between the extended IFC4 schema and gbXML was also developed to allow information exchange between BIM and energy simulation software. An example scenario is presented to illustrate the potential of the extended IFC4 schema for supporting energy analysis directly.

Understanding pedestrian behaviors such as their movement patterns in urban areas could contribute to the design of pedestrian-friendly facilities. With the commonly deployed surveillance cameras, pedestrian movement in a wide region could be identified by the person re-identification (ReID) technique across multiple cameras. Convolutional neural networks (CNNs) have been widely studied to automate the ReID task. CNN models equipped with deep learning techniques could extract discriminative human features from images and show promising ReID performance. However, some common challenges such as occlusion and appearance variation are still unsolved. Specifically, our study infers that over-relying on discriminative features only may compromise ReID performance. Therefore, this paper proposes a new model that extracts enriched features, which is more reliable against those ReID challenges. By adding a feature dropping strategy during model training, our model learns to focus on rich human features from different body parts. Moreover, this paper presents an explainable approach of model design, by visualizing which human parts a deep learning model focuses on. Based on an intuitive interpretation of model behaviors that lead to inaccurate results, specific improvement of model architecture is inspired. Our improved results suggest that making existing models explainable could effectively shed light on designing more robust models.

The layout design and spatial coordination of multiple pipe systems is major challenge in the construction industry. The purpose of multiple pipe layout design is to find out an optimal layout for numerous individual pipes to route from a different start locations to different end locations in a 3D environment with no clashes under various kinds of constraints. Currently, pipe layout design is conducted manually by consultants, which is tedious, labor intensive, error-prone, and time-consuming. This paper proposes a BIM-based approach for layout design of multiple pipes using heuristic search methods. Algorithms are developed based on a directed weighted graph according to the physical, design, economical and installation requirements of pipe layout design. Clashes between pipes and with building components are considered and subsequently avoided in the layout optimization. Based on the developed algorithms, simulated annealing (SA) algorithm is used to approximate global optimization in a large search space for multiple pipe layout optimization. As for layout design, Dijkstra algorithm and two heuristic algorithms namely 3D A* and fruit fly optimization algorithm (FOA) are implemented and compared to obtain the multiple pipe system layout design. An example of a typical plant room with nine pipe routes is used to illustrate the developed approach on multiple pipe layout design. The result shows that the developed approach can generate optimal and clash-free multiple pipe system layout. Compared with the conventional method, the developed approach significantly reduces the time and cost for designing multiple pipe layout.

Construction Document Management Systems (CDMS) track, manage, and store a variety of documents such as 3D model files, schedules, specifications, and images that are large in size and have different ownerships. The primary objectives of CDMSs are to facilitate document approval workflows, document version management, and security properties such as integrity of data, audit-trail, and approvals. A popular approach for CDMS is to consolidate all the project documents into an on premise or cloud-based centralized location that is owned and managed by a project participant or a trusted third party. Such centralized approaches for electronic documents pose risks such as single points of failures causing loss or corruption of documents and deliberate blocking of access during disputes between the owner of CDMS and other project participants. This situation is particularly challenging in construction projects where project participants cannot fully trust each other due to its inherently fragmented project organizational structure. Therefore, in this paper, a distributed construction document management system using Blockchain and distributed content-addressable storage technologies is presented. Blockchain is a peer-to-peer technology that facilitates distributed computation and irreversible data recording through smart contract and blockchain ledger technologies respectively. Immutability of records and computational logic is facilitated through the unique cryptographic data structure of blockchain ledgers and probabilistic consensus algorithms. In this paper, smart contracts are deployed to facilitate document approval workflows to support processes such as design review and request for information in construction projects. A blockchain ledger data model for tracking workflows and document version management is proposed. Public-key cryptography is deployed to facilitate data confidentiality and integrity in endorsements. A cryptographic indexing structure to support blockchain ledger in document versioning and to validate the authenticity of document search results for CDMS is proposed. The proposed framework also deploys peer-to-peer content-addressable storage for preventing single points of failure and data integrity of documents through data partitioning, data replication, and cryptography. The proposed CDMS is a distributed yet unified platform for managing construction documents. A demonstration of the proposed CDMS is presented with a case-based scenario on request for information (RFI) management.

Posture estimation provides basic mechanical information for remote control of construction equipment, safety analysis of construction sites and productivity analysis of construction projects. The rapid development of micro-machined electromechanical systems (MEMS) inertial sensors enables wide application of posture estimation in the construction industry. Previous studies have put efforts to orientation estimation, trajectory tracking, and activity recognition of construction equipment based on Inertia Measurement Units (IMU). As excavators are common and important heavy construction equipment on site, posture estimation of excavators is closely related to the safety of construction sites and automation of construction processes. Although there have been studies on IMU-based posture estimation of excavators, there is still a lack of studies that rigorously discuss the impact of IMU installation positions on posture estimation of construction equipment, especially excavators. Placing IMU sensors in appropriate positions on excavators would affect the accuracy of posture estimation. More practically, the installability issue also governs the installation positions. Hence, this paper investigates the influence of different IMU installation positions on posture estimation of excavators. Firstly, a systematic framework is proposed to estimate posture of excavators based on data collected by IMUs. Our experiments verify the feasibility of our proposed framework. Furthermore, the in-fluences of different installation positions of IMU are investigated, on the basis of accuracies represented by Euler angle. Considering accuracy and applicability, a scheme of installing IMU on excavators is discussed. This paper will provide a theoretical basis and a reliable operation scheme for IMU-based posture estimation of excavators in the future.

Construction sites suffer from higher hazard rates compared to other occupational workplaces, which can be attributed to dynamic activities of construction equipment. Hence, it is important to track locations, poses, and movements of construction equipment for on-site safety monitoring. With the wide installations of surveillance cameras, previous studies focused on tracking locations of construction equipment from videos using computer vision techniques. However, the location of many construction equipment remains unchanged during operation while the equipment poses are varying constantly. The variation of equipment poses can cause severe hazards, such as the collision with surrounding workers and other equipment. To avoid such potential hazards, it is important to monitor and forecast the poses of equipment. So far, there is limited research on automatically forecasting poses of construction equipment, which is necessary to provide hazard alerts and prevent spatial conflicts. Therefore, a vision-based pipeline is proposed in this paper to automatically forecast dynamic poses of construction equipment based on historical on-site surveillance videos. The proposed pipeline firstly utilizes a deep learning -based equipment pose estimation model to estimate poses of construction equipment so as to obtain historical poses of construction equipment. Then, one type of recurrent neural network (RNN), namely Gated Recurrent Unit (GRU), is adopted to learn the temporal features of the generated sequential poses and forecast potential poses of construction equipment. To validate the proposed method, a dataset containing equipment keypoint-based poses is created and annotated for training the model. The experiment results based on our created dataset demonstrate the capability of the proposed pipeline.

Computational fluid dynamics (CFD) is a powerful tool for performing indoor airflow analysis. The simulation results are usually validated with measurement results for accuracy in reflecting reality. When conducting CFD for simulating air flow in a multiple-zone indoor environment with different boundary conditions in different regions, the validation of the CFD model becomes sophisticated. To improve the accuracy of the simulation, boundary conditions need to be adjusted based on how significant the influence factors are affecting the multi-zone CFD model, which few studies have been conducted on. The objective of this study is to investigate the impact of influence factors on temperature and carbon dioxide concentration distribution of a validated CFD model of a typical office floor using ANSYS Fluent. This study provides insights on how to fine-tune a complex model to reflect the actual air flow and how the air quality and human comfort in different zones on the same floor could be affected by influence factors. The influence factors investigated are: (1) size of door gaps, (2) solar radiation and (3) number and orientation of occupants. The velocity variations caused by different door gap sizes were studied for improving multi-zone simulation accuracy by adjusting door gap sizes. To study the significant impact of solar heat on multi-zone environment, the sensitivity of different regions of the office floor to solar heat amount and distribution was analyzed by conducting solar analysis under different weather conditions. Impact of occupants on temperature and carbon dioxide concentration distributions in multi-zone environment were investigated by considering different numbers and facing directions of occupants in different regions of the office floor. In addition, this study demonstrates how to modify the influence factors efficiently using building information modeling (BIM).

A 5-year-old, clinically normal intact female Miniature Schnauzer was presented for demonstrative ultrasonography in a seminar. She had two pregnancies in the past and had a natural mating 2 months previously. Ultrasonography revealed a segmental and circumferential mural thickening of the right uterine horn. The endometrium was markedly thickened with multiple organized hyperechoic linear striations, perpendicular to the mucosal surface. Histology revealed focal endometrial hyperplasia resembling the maternal tissue of the normal canine placenta. A diagnosis of spontaneous pseudo-placentational endometrial hyperplasia (PEH) was made. This study described a unique ultrasonographic appearance of PEH, which may facilitate the diagnosis of PEH.

Automated interpretation of closed-circuit television (CCTV) inspection videos could improve the speed and consistency of sewer condition assessment. Previous approaches focus on defect cla...

In high-density, high-rise cities such as Hong Kong, buildings account for nearly 90% of energy consumption and 61% of the carbon emissions. Therefore, it is important to study the design of buildings, especially high-rise buildings, so as to achieve lower carbon emissions. The carbon emissions of a building consist of embodied carbon from the production of construction materials and operational carbon from energy consumption during daily operation (e.g., air-conditioning and lighting). While most of the previous studies concentrated mainly on either embodied or operational carbon, an integrated analysis of both types of carbon emissions can improve the sustainable design of buildings. Therefore, this paper presents a holistic framework using building information modeling (BIM) technology in order to enhance the sustainable low carbon design of high-rise buildings. BIM provides detailed physical and functional characteristics of buildings that can be integrated with various environmental modeling approaches to achieve a holistic design and assessment of low carbon buildings. In a case study, the proposed framework is examined to evaluate the embodied and operational carbon in a high-rise residential building due to various envelope designs. The results demonstrate how the BIM framework provides a decision support basis for evaluating the key carbon emission sources throughout a building's life cycle and exploring more environmentally sustainable measures to improve the built environment.

Although more than 65% of the total cost in facility management (FM) comes from facility maintenance management (FMM), there is a lack of efficient maintenance strategies and right decision making approaches to reduce FMM costs. Building information modeling (BIM) has been developed as a potential technology for FMM in buildings. This study proposes an FMM framework based on BIM and facility management systems (FMSs), which can provide automatic scheduling of maintenance work orders (MWOs) to enhance good decision making in FMM. In this framework, data are mapped between BIM and FMSs according to the Industry Foundation Classes (IFC) extension of maintenance tasks and MWO information in order to achieve data integration. After bi-directional data transmission between the BIM models and FMSs, work order information is visualized in BIM via API to identify components that have failed. Second, geometric and semantic information of the failure components is extracted from the BIM models to calculate the sub-optimal maintenance path in the BIM environment. Third, the MWO schedule is automatically generated using a modified Dijkstra algorithm that considers four factors, namely, problem type, emergency level, distance among components, and location. Illustrative examples are given in the paper to validate the feasibility and effectiveness of the proposed framework in indoor and outdoor 3D environments.

This paper proposes a robust method for multi-camera person re-identification (ReID), which can accurately track pedestrians across non-overlapping cameras. Closed-circuit television (CCTV) are widely used to capture pedestrian movement in different places. By integrating CCTV with computer vision and deep learning techniques, trajectory of individual pedestrian can be efficiently acquired for analyzing pedestrian walking behaviors. Many existing ReID methods aim to extract discriminative human features to distinguish a person from others. Recent state-of-the-art performance is achieved mostly by obtaining fine features from each body part. However, these part-based feature extraction methods did not consider which parts are more useful for person ReID. Therefore, this paper proposes a weighted-parts feature extraction method, such that features of specific body parts are more influential to identity prediction. After comparing the performances of utilizing each part alone, several parts are considered more view-invariant and discriminative. Higher weights are then imposed on these specific parts to extract more useful human features for person ReID. Experimental results with videos on a college campus show that the ReID accuracy of our proposed method notably outperforms many existing ones.

Design of steel reinforcement is an important and necessary task for designing reinforced concrete (RC) building structures. Currently, steel reinforcement design is performed manually or semi-automatically using computer software such as ETABS, with reference to building codes. These approaches are time consuming and sometimes error-prone. Recent advances in building information modeling (BIM) technology allow digital 3D BIM models to be leveraged for supporting different types of engineering analyses such as structural engineering design. With the aid of BIM technology, steel reinforcement design could be automated for fast, economical and error-free procedures. This paper presents a BIM-based framework using the developed three-stage hybrid genetic algorithm (GA) for automated optimization of steel reinforcement in RC frames. The methodology framework determines the selection and alignment of steel reinforcement bars in an RC building frame for the minimum steel reinforcement area, considering longitudinal tensile, longitudinal compressive and shear steel reinforcement. The first two stages optimize the longitudinal tensile and longitudinal compressive steel reinforcement while the third stage optimizes the shear steel reinforcement. International design code (BS8110) and buildability constraints are considered in the developed optimization framework. A BIM model in Industry Foundation Classes (IFC) is then automatically created to visualize the optimized steel reinforcement design results in 3D thereby facilitating design communication and generation of construction detailing drawings. A three-storey RC building frame is analyzed to check the applicability of the developed framework and its improvement over current design approaches. The results show that the developed methodology framework can minimize the steel reinforcement area quickly and accurately.

Quantification and assessment of nationwide population access to health-care services is a critical undertaking for improving population health and optimizing the performance of national health sys...

Steel reinforcement (also referred to as rebar) design plays an important role in reinforced concrete (RC) structures in terms of strength requirements, cost and buildability. Currently, rebar design is performed at the individual member level with few considerations of the interference between different intersecting members, leading to potential clashes and buildability issues. Design for Manufacture and Assembly (DfMA) is a cutting-edge technology concept that improves the design for higher buildability by incorporating knowledge from downstream manufacture and assembly activities into the early design phase. This paper presents an innovative DfMA-oriented approach for rebar design optimization with Building Information Modeling (BIM) and a hybrid metaheuristic algorithm. Firstly, activities related to the manufacture and assembly of rebar are identified to apply DfMA principles, followed by the proposed BIM-based optimization framework. Secondly, multi-objective cost design formulation including both the material cost and the installation cost is proposed, incorporating the design code requirements and DfMA considerations. Following this, key modules for implementation are presented including the rebar layout searching, the hybrid genetic algorithm incorporated with Hooke and Jeeves’s method for optimization and rebar clash avoidance. The illustrative example shows that the proposed approach can generate a series of practical rebar layouts, and a trade-off between the material cost and installation cost is found, which means excessive emphasis on minimizing one of them will lead to the rise of the other.

Existing platforms for collaborative BIM design have a centralized system architecture, which suffers cybersecurity risks of design data manipulation and denial of access, leading to a loss of data traceability, a decline in design productivity, and project delays. Blockchain is a promising technology to solve such risks by providing decentralized and immutable data storage. However, integrating blockchain with BIM faces a problem that blockchain is inherently unsuitable for storing large-sized design files like BIM models, hindering blockchain from protecting BIM data integrity. Therefore, this paper proposes a distributed common data environment (DCDE) framework for BIM-based design leveraging two distributed technologies: blockchain and Interplanetary File System (IPFS). The DCDE framework guarantees irreversible design changes storage using blockchain while secures design file storage using IPFS. A blockchain transaction data model and a smart contract are also developed within the framework to support DCDE functionalities. Lastly, framework applicability and performance are tested in an illustrative design example. Results show that: (1) DCDE is a feasible solution for secure design collaboration, and (2) DCDE latency (in millisecond-level), TPS (60 transactions per second), and storage cost (12.5 KB per day) are within an acceptable range.

Currently, as-built building information modeling (BIM) models from point clouds show great potential in managing building information. The automatic creation of as-built BIM models from point clouds is important yet challenging due to the inefficiency of semantic segmentation. To overcome this challenge, this paper proposes a novel deep learning-based approach, ResPointNet++, by integrating deep residual learning with conventional PointNet++ network. To unleash the power of deep learning methods, this study firstly builds an expert-labeled high-quality industrial LiDAR dataset containing 80 million data points collected from four different industrial scenes covering nearly 4000 m2. Our dataset consists of five typical semantic categories of plumbing and structural components (i.e., pipes, pumps, tanks, I-shape and rectangular beams). Second, we introduce two effective neural modules including local aggregation operator and residual bottleneck modules to learn complex local structures from neighborhood regions and build up deeper point cloud networks with residual settings. Based on these two neural modules, we construct our proposed network, ResPointNet++, with a U-Net style encoder-decoder structure. To validate the proposed method, comprehensive experiments are conducted to compare the robustness and efficiency of our ResPointNet++ with two representative baseline methods (PointNet and PointNet++) on our benchmark dataset. The experimental results demonstrate that ResPointNet++ outperforms two baselines with a remarkable overall segmentation accuracy of 94% and mIoU of 87%, which is 23% and 42% higher than that of conventional PointNet++, respectively. Finally, ablation studies are performed to evaluate the influence of design choices of the local aggregation operator module including input feature type and aggregation function type. This study contributes to automated 3D scene interpretation of industrial point clouds as well as the as-built BIM creation for industrial components such as pipes and beams.

Understanding the impact of material choices and structural forms on the embodied carbon and construction cost in high-rise buildings is important to improve building designs with regard to sustainability. The objective of this study is to investigate the impact of the choice of construction materials and structural forms on the embodied carbon and construction cost of high-rise buildings. The results show that high-rise buildings using structural steel have the highest construction cost at 4575 HK$/m2 and the most embodied carbon at 760 kg CO2-e/m2, respectively. Using reinforced concrete for high-rise buildings reduces 30% of the embodied carbon (to 4194 HK$/m2) and 7% of the construction cost (to 537 kg CO2-e/m2). High-rise buildings using composite materials have the lowest construction cost (3740 HK$/m2), but produce slightly more embodied carbon (557 kg CO2-e/m2) than concrete buildings. For a specific structural form, the construction cost and the embodied carbon as a function of the buildi...

It has been suggested that metastasis-associated proteins 1 and 2 (MTA1 and MTA2) are capable of suppressing estrogen receptor alpha (ERα) transactivation activity in breast cancer cells. ERα, which is present in the epididymis, is a crucial mediator of maintaining the luminal environment necessary for proper sperm maturation and function. The present study was undertaken to analyze the expression profile of both MTA1 and MTA2 in the epididymis of rats and to ascertain whether MTA1/2 colocalizes with ERα in the epididymis and primary cultured epididymal epithelial cells. Reverse transcription polymerase chain reaction (RT-PCR), Western blotting and immunohistochemistry analyses were utilized to demonstrate that MTA1 and MTA2 are expressed in the epididymis. Furthermore, these analyses revealed that MTA1 and MTA2 are predominantly localized in the nuclei of almost all epididymal epithelial cells. Immunofluorescence staining revealed that MTA1/2 colocalizes with ERα in epididymal epithelial cells. In conclusion, MTA1 and MTA2 are expressed in the epididymis of rats; these proteins colocalize with ERα in epididymal epithelial cells, suggesting that MTA1 and MTA2 may be involved in the regulation of ERα transactivation activity in the epididymis of rats to facilitate a stable environment in the lumen.

Analyzing the walking behavior of the public is vital for revealing the need for infrastructure design in a local neighborhood, supporting human-centric urban area development. Traditional walking behavior analysis practices relying on manual on-street surveys to collect pedestrian flow data are labor-intensive and tedious. On the contrary, automated video analytics using surveillance cameras based on computer vision and deep learning techniques appears more effective in generating pedestrian flow statistics. Nevertheless, most existing methods of pedestrian tracking and attribute recognition suffer from several challenging conditions, such as inter-person occlusion and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian flow statistics. Therefore, this paper proposes a more robust methodology of pedestrian tracking and attribute recognition, facilitating the analysis of pedestrian walking behavior. Specific limitations of a current state-of-the-art method are inferred, based on which several improvement strategies are proposed: 1) incorporating high-level pedestrian attributes to enhance pedestrian tracking, 2) a similarity measure integrating multiple cues for identity matching, and 3) a probation mechanism for more robust identity matching. From our evaluation using two public benchmark datasets, the developed strategies notably enhance the robustness of pedestrian tracking against the challenging conditions mentioned above. Subsequently, the outputs of trajectories and attributes are aggregated into fine-grained pedestrian flow statistics among different pedestrian groups. Overall, our developed framework can support a more comprehensive and reliable decision-making for human-centric planning and design in different urban areas. The framework is also applicable to exploiting pedestrian movement patterns in different scenes for analyses such as urban walkability evaluation. Moreover, the developed mechanisms are generalizable to future researches as a baseline, which provides generic insights of how to fundamentally enhance pedestrian tracking.