BcBIM: A Blockchain-Based Big Data Model for BIM Modification Audit and Provenance in Mobile Cloud

Building Information Modeling (BIM) is envisioned as an indispensable opportunity in the architecture, engineering, and construction (AEC) industries as a revolutionary technology and process. Smart construction relies on BIM for manipulating information flow, data flow, and management flow. Currently, BIM model has been explored mainly for information construction and utilization, but rare works pay efforts to information security, e.g., critical model audit and sensitive model exposure. Moreover, few BIM systems are proposed to chase after upcoming computing paradigms, such as mobile cloud computing, big data, blockchain, and Internet of Things. In this paper, we make the first attempt to propose a novel BIM system model called bcBIM to tackle information security in mobile cloud architectures. More specifically, bcBIM is proposed to facilitate BIM data audit for historical modifications by blockchain in mobile cloud with big data sharing. The proposed bcBIM model can guide the architecture design for further BIM information management system, especially for integrating BIM cloud as a service for further big data sharing. We propose a method of BIM data organization based on blockchains and discuss it based on private and public blockchain. It guarantees to trace, authenticate, and prevent tampering with BIM historical data. At the same time, it can generate a unified format to support future open sharing, data audit, and data provenance

Stochastic Characteristics of Manual Solar Shades and their Influence on Building Energy Performance

Occupant behavior has a significant impact on building energy performance. The purpose of this paper is to quantify the stochastic characteristics of manual solar shades and their influence on building energy performance. A co-simulation for occupants’ stochastic control of manual solar shades was conducted and the statistic indicators (non-parameter tests and autocorrelation function) were calculated in order to identify potential occupant behavior patterns. The results show that occupants’ stochastic shade control behavior among different seasons is not statistically different and that shade control behavior is not completely stochastic. Meanwhile, the trend in the fluctuation of Sc changes with time. Furthermore, a new index was introduced to evaluate the effectiveness of manual solar shades in terms of energy performance. The result shows that the effectiveness of manual solar shades is only between 39.8% and 81.3%, compared with automatically controlled shades, and there is a large potential for improving the effectiveness of manual solar shades in different seasons

Energy and Economic Performance of Solar Cooling Systems in the Hot-Summer and Cold-Winter Zone

Building energy consumption has distinctly increased in the hot-summer and cold-winter zone in China. Solar cooling technology has been developed to reduce the increasing electricity consumption for air conditioning and to shift the peak load during hot summer days. This paper presents a performance simulation and economic analysis for both photovoltaic (PV) and thermal solar cooling systems compared to a reference system, which is composed of two electric heat pumps. The results show that 30.7% and 30.2% of primary energy can be saved by using the PV and the thermal system, respectively. The payback time is 6–7 years for the PV system, but more than 20 years for the thermal system based on current conditions in China. Therefore, the PV system is more suitable for practical application in the hot-summer and cold-winter zone. The thermal system could be an alternative when middle- and high-temperature solar thermal collector technology has been further developed, as well as following mass production of small- and middle-sized chillers

Forecast of Energy Consumption and Carbon Emissions in China’s Building Sector to 2060

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies

Forecast of Energy Consumption and Carbon Emissions in China’s Building Sector to 2060

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies

Installation and Operation of a Solar Cooling and Heating System Incorporated with Air-Source Heat Pumps

A solar cooling and heating system incorporated with two air-source heat pumps was installed in Ningbo City, China and has been operating since 2018. It is composed of 40 evacuated tube modules with a total aperture area of 120 m2, a single-stage and LiBr–water-based absorption chiller with a cooling capacity of 35 kW, a cooling tower, a hot water storage tank, a buffer tank, and two air-source heat pumps, each with a rated cooling capacity of 23.8 kW and heating capacity of 33 kW as the auxiliary system. This paper presents the operational results and performance evaluation of the system during the summer cooling and winter heatingperiod, as well as on a typical summer day in 2018. It was found that the collector field yield and cooling energy yield increased by more than 40% when the solar cooling and heating system is incorporated with heat pumps. The annual average collector efficiency was 44% for cooling and 42% for heating, and the average coefficient of performance (COP) of the absorption chiller ranged between 0.68 and 0.76. The annual average solar fraction reached 56.6% for cooling and 62.5% for heating respectively. The yearly electricity savings accounted for 41.1% of the total electricity consumption for building cooling and heating

Model Tests on Jacked Pile Penetration Characteristics Considering a Static Press-in Piling Machine

This study incorporates a static press-in piling machine into the conventional laboratory model tests for jacked piles. By conducting a comparative analysis between two tests, one involving the static press-in piling machine and the other focusing solely on pile jacking, this study aims to unveil the variations in penetration characteristics with pile sinking depth during the process of pile jacking under the constraint imposed by the static press-in piling machine. When considering the impact of the piling machine, the pile pressing force, pile sinking resistance, pile axial force, and unit side friction resistance of the pile body are higher compared to test results that only focus on pile jacking. There is an acceleration in the total side friction resistance within the depth range of 20 to 30 cm. Additionally, the reduction rate of axial force during the entire pile jacking process is 2% higher, with a general reduction in the “side resistance degradation” phenomenon. The soil pressure around the pile exhibits an initial increase followed by a decrease. The authors believe that the model box test of the jacked pile, considering the pile machine, would be more aligned with engineering practice

Neutrophil-lymphocyte ratio as a predictive marker for postoperative infectious complications: A systematic review and meta-analysis

Objective: Postoperative infection is a common but costly complication. The neutrophil-lymphocyte ratio is a promising marker for the identification of postsurgical infectious events. We aimed to perform this meta-analysis to assessed the accuracy of the neutrophil-lymphocyte ratio for the prediction of postsurgical infection. Methods: We searched PubMed, Embase, Web of Science, and Cochrane Library without language restriction from their inceptions to April 2022, and checked reference lists of included studies. Studies were included if they assessed predictive accuracy of neutrophil-lymphocyte ratio for postsurgical infection. We estimated its predictive value and explored the source of heterogeneity. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess methodological quality and the Deeks’ test to evaluate publication bias. The bivariate model and hierarchical summary receiver operating characteristic (HSROC) curve were used for meta-analysis and generated a summary receiver operating characteristic space (ROC) curve. Results: Our search returned 379 reports, of which 12 fulfilled the inclusion criteria, accounting for 4375 cases. The bivariate analysis yielded a pooled sensitivity of 0.77 (95%C.I.: 0.65–0.85) and specificity of 0.78 (95%C.I.: 0.67–0.86). Pooled positive LR and negative LR were 3.48 (95%C.I.: 2.26–5.36) and 0.30 (95%C.I.: 0.20–0.46), respectively. A negative LR of 0.30 reduces the post-test probability to 2% for a negative test result. The area under of receiver operating characteristic curve was 0.84 (95%C.I.: 0.80–0.87). Subgroups comparisons revealed difference by study design, surgical site, presentence of implant, time of sampling, type of infection event and prevalence of infection. The Deeks’ test showed no publication bias. The sensitivity analysis showed no study affected the robustness of combined results. Conclusions: Low-certainty evidence suggests that the neutrophil-lymphocyte ratio is a helpful marker for predicting postoperative infectious complication. The negative predictive value of the neutrophil-lymphocyte ratio enables for reliable exclusion of postoperative infection.Trial registrationPROSPERO registration number CRD42022321197. Registered on 27 April 2022