Novel energy-saving window coating based on F doped TiO2 nanocrystals with enhanced NIR shielding performance

To reduce the energy consumption of cooling in the hot summer days, searching for novel NIR shielding materials for buildings is of great value. In this report, monodispersed F doped TiO₂ nanocrystals with an average size of 8.6 nm were synthesized as novel solar shielding materials for energy-saving windows. All the products adopted an anatase TiO₂ structure. After doping of F ions, the morphology of TiO₂ was transformed from an irregular shape to a pseudospherical shape. The Raman shift and XPS depth analysis confirmed the successful doping of F⁻ ions into the lattice oxygen sites in the TiO₂ structure. The introduction of F⁻ ions generated free electrons and bulk Ti³⁺ in TiO₂ crystals, which activated a localized surface plasmon resonance (LSPR) absorption in the NIR region. Correspondingly, the NIR shielding performance of the TiO₂ films improved with increasing F doping amounts. The NIR shielding value of the films increased from 1.3% to 43.2% when the molar ratio of F to Ti increased from 0 to 0.3. The reason can be attributed to the enhanced NIR absorption induced by the increased electron concentration after doping of fluorine ions. The F–TiO₂ films showed superior visible transmittance (90.1–96.7%). Moreover, the F–TiO₂ films lowered the indoor temperature of the heat box by 5.3 °C in the thermal tests. Overall, the prepared F–TiO₂ nanocrystals show a great potential to be used for energy-saving windows.     

Smart work packaging-enabled constraint-free path re-planning for tower crane in prefabricated products assembly process

Lack of constraint-free crane path planning is one of the critical concerns in the dynamic on-site assembly process of prefabrication housing production (PHP). For decades, researchers and practitioners have endeavored to improve both the efficiency and safety of crane path planning from either static environment or re-planning the path when colliding with constraints or periodically updating the path in the dynamic environment. However, there is a lack of approach related to the in-depth exploration of the nature of dynamic constraints so as to assist the crane operators in making adaptive path re-planning decisions by categorizing and prioritizing constraints. To address this issue, this study develops the smart work packaging (SWP)-enabled constraints optimization service. This service embraces the core characteristics of SWP, including adaptivity, sociability, and autonomy to achieve autonomous initial path planning, networked constraints classification, and adaptive decisions on path re-planning. This service is simulated and verified in the BIM environment, and it is found that SWP-enabled constraints optimization service can generate the constraint-free path when it is necessary.     

电力系统自动化发展方向分析

计算机技术、控制技术及信息技术的发展,电力系统自动化面临着空前的变革,多媒体技术、智能控制技术将迅速进入电力系统自动化领域。     

Convolutional neural network: Deep learning-based classification of building quality problems

The rapid development of the construction industry in China has introduced unprecedented quality-related problems in the country’s building industry. In response to this issue, the government has established various complaint channels to report quality problems. Therefore, building quality complaints (BQCs) need to be classified and solved by respective agencies or departments rapidly for avoiding adverse impact on the safety, health, and well-being of people. However, the current process of classifying BQCs is labor intensive, time consuming, and error prone. An automatic complaint classification is required to improve the effectiveness and efficiency of complaint handling, but studies on this issue are limited. Prevailing text classification research in construction has focused on utilizing conventional shallow machine learning. By contrast, this study explores a novel convolutional neural network (CNN)-based approach that incorporates a deep-learning method to automatically classify the short texts contained within BQCs. The presented approach enables capturing the semantic features in BQC texts and automatic classification of the BQCs into predefined categories. After the model optimization, tests are conducted to examine the practical application of the text classification approach compared with Bayes-based and support vector machine classifiers. Results indicate that the developed CNN-based approach performs well in the Chinese BQC classification with limited manual intervention and few complicated feature engineering.     

CME Forum: a response to “Construction flow index: a metric of production flow quality in construction”

The study of types of flow in construction is a relatively new field. This paper reviews the work of Sacks et al. and reveals that they have applied a production-line metaphor with recognition of two production flows. Process flow: locations are equated with products moving down a production line. Operations flow: work crews are equated to work stations. Their work proposes an index of the quality of production flow in construction, but the research design has three significant flaws: the skilled interpretation of flow-line charts is not in turn applied to the interpretation of their example charts; the conceptual framework does recognize that the underlying metaphor requires levels of detail in both location and task that is not supported in their analysis. The meaning of “quality of flow” in this context is not defined. This debate raises important epistemological questions for those working in lean construction and location-based management. While the concept of “production flow quality” is important, the Sacks et al. methodology does not address the detailed planning of individual crews. It is only possible to apply the production-line metaphor if micro-management is adopted as detailed planning.     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

Operation of a stationary hydrogen energy system using TiFe-based alloy tanks under various weather conditions

We describe the operation of a bench-scale stationary hydrogen energy system comprising photovoltaic (PV) panels, a water electrolyzer (Ely), metal hydride tanks fabricated using an AB-type TiFe-based alloy (TiFe-based tanks), fuel cells (FC), and batteries under various weather conditions. The FC and TiFe-based tanks are thermally coupled to transfer heat when necessary to stabilize the output power, and automatic control is provided via a building energy management system (BEMS), which plans the operating schedule up to 48 h in advance based on the weather forecast and expected demands of the building. Experiments were conducted for 24-h operation on a fine day, 48-h operations on partly cloudy and partly cloudy days, and 48-h operations on partly cloudy and rainy days in order to verify the system. Each operation was performed as planned. Our results show that it is possible to operate the hydrogen system all year round without external heat sources.     

State of research in automatic as-built modelling

Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.     

Comprehensive recovery of metals from cyanidation tailing

Cyanidation tailing is the residue produced in gold plants which use cyanidation to extract gold. It can be used as a secondary resource to recover residual metals that are of great economic value. The cyanidation tailing investigated in this paper was obtained from Shandong Province, China. It contained valuable metals such as chalcopyrite, galena, sphalerite and pyrite. In this study, alkaline sodium hypochlorite was used as a regulator in the pretreatment stage. It was proved that the sodium hypochlorite played two roles in the flotation pulp: oxidant and pH regulator. On one hand, sodium hypochlorite oxidized cyanide to cyanate, eliminating the negative effect of residual cyanide towards the environment. On the other hand, with the pH of flotation pulp exceeding 10, sphalerite and pyrite were depressed enormously, which was beneficial to the recovery of chalcopyrite and galena. With the Cu–Pb bulk flotation flowsheet, the cyanidation tailing was processed to obtain qualified Cu concentrate with grade of 13.17% and recovery of 70.00% compared with the original Cu grade of 0.21%. The Cu–Pb tailing was processed to obtain qualified Zn concentrate with grade of 34.72% and recovery of 69.58% compared with the original Zn grade of 0.33%, constituting the comprehensive recovery routing for the cyanidation tailing.