Enhancing BIM security in emergency construction projects using lightweight blockchain-as-a-service.

Rapid design and construction of mobile cabin hospitals (MCHs) have become imperative in the COVID-19 response. However, due to unique design specifications (e.g., parallel design and model pre-revision), collaboration in emergency construction projects (ECPs) like MCHs presents data security vulnerabilities, including a lack of traceability and transparency. These hazards invariably reduce design effectiveness, leading to undesirable rework and project delay. Blockchain technology is a potential solution to address the aforementioned security issues in ECPs because it offers immutable and traceable data storage. Nevertheless, directly implementing blockchain in ECPs is impractical, for the blockchain has a complex deployment process and provides limited functions supporting BIM-based design. Therefore, this paper develops a lightweight blockchain-as-a-service (LBaaS) prototype to enhance the ECPs design efficiency by securing and automating information exchange while eliminating the difficulties of deploying and using blockchain. This paper contributes three elements: (1) Security vulnerabilities of design in ECP are identified. Taking an MCH in Hong Kong as an example, this paper investigates its design process and determines two design characteristics and associated security flaws. (2) Key technologies to support easy deployment and usage of blockchain in ECPs are developed. New technical elements, including a Multi-to-One mapping (MtOM) kit for easy blockchain registration, an integrated workflow retaining existing design practices, and smart contracts for secure interaction with blockchain, are developed to support LBaaS functionality. (3) An LBaaS prototype is validated and evaluated. The prototype is illustrated and evaluated using design examples based on actual MCH project data. Results show that the LBaaS is a feasible and secure approach for ECPs collaboration. This paper deepens the understanding of data security issues in ECPs and offers technical guidance in establishing blockchain solutions.

Sensitivity analysis of influence factors on multi-zone indoor airflow CFD simulation.

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).

Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network.

In the last decades, air pollution has been a critical environmental issue, especially in developing countries like China. The governments and scholars have spent lots of effort on controlling air pollution and mitigating its impacts on human society. Accurate prediction of air quality can provide essential decision-making supports, and therefore, scholars have proposed various kinds of models and methods for air quality forecastings, such as statistical methods, machine learning methods, and deep learning methods. Deep learning-based networks, such as RNN and LSTM, have been reported to achieve good performance in recent studies. However, the excellent performance of these methods requires sufficient data to train the model. For stations that lack data, such as newly built monitoring stations, the performance of those methods is constrained. Therefore, a methodology that could address the data shortage problem in new stations should be explored. This study proposes a transfer learning-based stacked bidirectional long short term memory (TLS-BLSTM) network to predict air quality for the new stations that lack data. The proposed method integrates advanced deep learning techniques and transfer learning strategies to transfer the knowledge learned from existing air quality stations to new stations to boost forecasting. A case study in Anhui, China, was conducted to evaluate the effectiveness of TLS-BLSTM. The results show that the proposed method can help achieve 35.21% lower RMSE on average for the experimented three pollutants in new stations.

Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques.

To better control and manage harbor water quality is an important mission for coastal cities such as New York City (NYC). To achieve this, managers and governors need keep track of key quality indicators, such as temperature, pH, and dissolved oxygen. Among these, the Biochemical Oxygen Demand (BOD) over five days is a critical indicator that requires much time and effort to detect, causing great inconvenience in both academia and industry. Existing experimental and statistical methods cannot effectively solve the detection time problem or provide limited accuracy. Also, due to various human-made mistakes or facility issues, the data used for BOD detection and prediction contain many missing values, resulting in a sparse matrix. Few studies have addressed the sparse matrix problem while developing statistical detection methods. To address these gaps, we propose a deep learning based model that combines Deep Matrix Factorization (DMF) and Deep Neural Network (DNN). The model was able to solve the sparse matrix problem more intelligently and predict the BOD value more accurately. To test its effectiveness, we conducted a case study on the NYC harbor water, based on 32,323 water samples. The results showed that the proposed method achieved 11.54%-17.23% lower RMSE than conventional matrix completion methods, and 19.20%-25.16% lower RMSE than traditional machine learning algorithms.

Quantification of construction waste prevented by BIM-based design validation: Case studies in South Korea.

Waste generated in construction and demolition processes comprised around 50% of the solid waste in South Korea in 2013. Many cases show that design validation based on building information modeling (BIM) is an effective means to reduce the amount of construction waste since construction waste is mainly generated due to improper design and unexpected changes in the design and construction phases. However, the amount of construction waste that could be avoided by adopting BIM-based design validation has been unknown. This paper aims to estimate the amount of construction waste prevented by a BIM-based design validation process based on the amount of construction waste that might be generated due to design errors. Two project cases in South Korea were studied in this paper, with 381 and 136 design errors detected, respectively during the BIM-based design validation. Each design error was categorized according to its cause and the likelihood of detection before construction. The case studies show that BIM-based design validation could prevent 4.3-15.2% of construction waste that might have been generated without using BIM.

A BIM-based system for demolition and renovation waste estimation and planning.

Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C&D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D&R) works and the growing amount of D&R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D&R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D&R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control.