Buildings In Shanghai Research Articles

Database Analysis of Long-Term Settlement Behavior of Pile Foundations in Shanghai Soft Coastal Clays

A database of 195 field records of long-term settlement of high-rise buildings in Shanghai soft coastal clays is presented. The collected field records are divided into two categories based on pile type, i.e., bored pile and precast pile. Based on the settlement recorders collected, the long-term settlement behavior and settlement duration of the pile foundations in soft coastal clays are investigated. The statistical results show that the pile foundation in Shanghai soft coastal clays can be divided into three kinds of site conditions by considering the pile length and variation of long-term deformation behavior. Prediction models based on the power function are proposed to calculate the normalized final settlement for both bored pile and precast pile foundations. Based on the statistical results, the average normalized final settlement (settlement per floor), average settlement ratio (final settlement to the settlement when the superstructure is completed), and average settlement duration in the three site conditions for both bored pile and precast pile foundations are given. Additionally, an exponential settlement prediction model that can accurately forecast the entire settlement and post-construction settlement characteristics of buildings in Shanghai’s soft soil area is proposed. These statistical results can be referred to the preliminary foundation design works, which contribute to the quick evaluation of the possible settlement range, settlement ratio, and settlement duration of the proposed building constructed in the soft coastal clay area in Shanghai.

Life cycle carbon emission calculation model of energy system in public buildings: A case study in Shanghai

Energy consumption and carbon emissions (CE) in the building sector have grown in importance in relation to global climate change for China's sustainable development. Relatively few research have addressed the embodied CE, despite the fact that many have examined the operational CE from energy system operational use. In order to determine the CE of the energy production, transportation, construction, operation, and recycling stages, we therefore put up an integrated model. An inventory analysis of the energy system's life cycle CE, a scenario forecast of various combinations of building energy system equipment, and a case study of the building energy system of a typical office building in Shanghai's service industry. According to the results, the embodied CE emits 38.01 % of its energy, and the operational CE emits 61.99 %. However, we should not underestimate the significance of the impact of the embodied CE. Among all projection scenarios, the suggested CE share is highest (18.40 %) when the system combination of air-source heat pump units, LED lights, and ground-source heat pump units is implemented. In the end, based on the structural analysis of the embodied CE, low-carbon growth strategies and policy recommendations for Shanghai's service sector will be made.

Optimal design of building envelope towards life cycle performance: Impact of considering dynamic grid emission factors

Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.

A case study of multi-objective design optimization of a healthy building in Shanghai, China

Energy efficient healthy buildings design is important in achieving carbon neutrality and occupants’ health, yet not sufficiently explored. This paper aims to optimize a healthy building in Shanghai, China, based on energy consumption, indoor air quality and visual comfort. A four-step optimization method was proposed. Firstly, Latin Hypercube-Soubert sampling method was used to generate 375 design samples and performance data through computer simulation and calculation. Secondly, five different machine learning approaches were applied for prediction model development. Thirdly, the best prediction models were coupled with eleven optimization algorithms to find Pareto solutions. Finally, optimization on different combinations of design parameters were conducted. It was found that the back propagation neural network and Pareto Envelope-based Selection Algorithm II were the best prediction models and optimization algorithm. The average reduction of building energy consumption, visual discomfort, and improper indoor air quality hours were found to be 25.73 %, 46.24 %, and 38.34 %, respectively. The recommended windows to wall ratios of the east, south, west and north walls, absorptance of solar radiation and filter types are in the range of 20%–35 %, 10%–45 %, 10%–30 %, 10%–40 %, 0.7–0.9, and S7-S9, respectively. This study contributes to enrich the case study of healthy buildings, provide a quick design optimization approach and analysis on the importance of each design parameter. Its originality lies in the optimization methodology, comparative analysis of prediction models, optimization algorithms, and combination effects of design parameters. The outcomes can guide the building designers in performing energy efficient design while maximizing indoor air quality and visual comfort.

Thermal performance of phase-change wall of a hotel building under hot-summer and cold-winter climate

ABSTRACT In China, the energy consumption in the building sector has increased tremendously in the past decades. It is important to investigate advanced passive energy-saving technologies for buildings. By applying phase-change materials in walls, the peak cooling/heating load can be significantly reduced. In the present study, a numerical model of one-dimensional heat transfer within a phase-change wall was developed and solved by Matlab, which was successfully validated with published data. Subsequently, the model was used to simulate the thermal performance of a phase-change wall in a hotel building in Shanghai, China. Simulation results demonstrate that the performance of the phase-change wall is optimized when the phase-change material is applied to both sides of the wall. Year-round heat transfer is minimized using layers of paraffin (C16-C28) and capric acid (2 cm), with phase-change temperatures of 43℃ and 16.3℃, respectively. The PCM wall reduces inwardthe inward heat transfer by 26.9% compared with a common wall without phase-change material. The outward heat loss is also reduced by 15.7% during the heating season. This study demonstrates that the PCM wall can effectively reduce indoor heat gain in summerin the summer and heat loss in winterin the winter. The numerical model has proven to be feasible for optimizing construction design.

Advancing Sustainability: Paper for Enhancing Eco-Friendly Methodology in the Shanghai Tower

This proposal aims to enhance the eco-friendly methods of the Shanghai Tower, a prominent building in Shanghai, by implementing additional sustainable practices. The building already incorporates various environmental friendly technologies, such as vertical green vegetation, intelligent building skin, natural ventilation, passive solar heating, and more. This proposal suggests further enhancements, including optimization natural lighting, rainwater collection, ice-need cold technology, on-site renewable energy utilization, and intelligent control systems. By implementing these measures, the Shanghai Tower can emerge as a pioneering model for other buildings, making significant contributions to environmental preservation, energy efficiency, and sustainable development in the region.

Evaluating and Comparing Human Perceptions of Streets in Two Megacities by Integrating Street-View Images, Deep Learning, and Space Syntax

Street quality plays a crucial role in promoting urban development. There is still no consensus on how to quantify human street quality perception on a large scale or explore the relationship between street quality and street composition elements. This study investigates a new approach for evaluating and comparing street quality perception and accessibility in Shanghai and Chengdu, two megacities with distinct geographic characteristics, using street-view images, deep learning, and space syntax. The result indicates significant differences in street quality perception between Shanghai and Chengdu. In Chengdu, there is a curvilinear distribution of the highest positive perceptions along the riverfront space and a radioactive spatial distribution of the highest negative perceptions along the ring road and main roads. Shanghai displays a fragmented cross-aggregation and polycentric distribution of the streets with the highest positive and negative perceptions. Thus, it is reasonable to hypothesize that street quality perception closely correlates with the urban planning and construction process of streets. Moreover, we used multiple linear regression to explain the relationship between street quality perception and street elements. The results show that buildings in Shanghai and trees, pavement, and grass in Chengdu were positively associated with positive perceptions. Walls in both Shanghai and Chengdu show a consistent positive correlation with negative perceptions and a consistent negative correlation with other positive perceptions, and are most likely to contribute to the perception of low street quality. Ceilings were positively associated with negative perceptions in Shanghai but are not the major street elements in Chengdu, while the grass is the opposite of the above results. Our research can provide a cost-effective and rapid solution for large-scale, highly detailed urban street quality perception assessments to inform human-scale urban planning.

Exploring the Impact of Facade Color Elements on Visual Comfort in Old Residential Buildings in Shanghai: Insights from Eye-Tracking Technology

Building façade color plays a key role in shaping urban image, enhancing urban vitality, and optimizing citizens’ living experience. Moreover, colors can influence people’s perception of space, but the multiple interrelationships between color elements and users’ color evaluation and visual perception have not yet been thoroughly studied. In order to explore the relationships between color elements and visual perception and subjective comfort, this study discusses the matching relationship between color and the comfort of a residential building façade from three levels of color hue, lightness, and saturation by combining eye movement technology and a subjective evaluation. Taking Anshan San Village in Shanghai as the research object, the relationship model between color elements, visual perception, and subjective comfort is established through correlation analysis, linear regression, and multifactor regression analysis, and the correlation between single elements and multiple elements is explored from multi-dimensional perspectives. It aims to assess the operability of eye movement data as a measure of the visual comfort with the residential building façade color through multiple indicators and to explore and establish a new correlation assessment framework between the three attributes of façade color and users’ visual comfort. The results show that (1) highly saturated façade colors significantly increase cognitive load and decrease color evaluation. (2) Brightness has a non-linear relationship with visual perception and subjective evaluation, either too high or too low a brightness value will cause visual fatigue and thus reduce the color evaluation, and when the brightness is maintained at 40–80%, there will be a better color experience. (3) Warm tones such as red, orange, and yellow can strengthen the color perception to enhance the color evaluation of the façade; among cold tones, cyan and purple can also strengthen the visual perception of the color of the façade to enhance the color evaluation. After validation, the proposed new color comfort assessment method is feasible and comprehensive, and we hope it can provide a reference for the planning of old residential buildings and communities in the future.

Seismic fragility analysis of human perception in high‐rise buildings subjected to far‐field long‐period earthquake ground motions: A case study for Shanghai tower

AbstractHuman perception in high‐rise buildings during far‐field long‐period earthquake ground motions often induces psychological discomfort, including fear and anxiety. A comprehensive understanding on human perception of seismic events is vital for city planning and emergency management, facilitating effective evacuation plans and minimizing stampede risks. This study addresses a gap by employing the seismic fragility analysis method to delineate how human perception in high‐rise buildings is affected during far‐field long‐period ground motions. The methodology involves several key steps. First, the far‐field long‐period earthquake ground motion was identified by detecting the later‐arriving surface waves, and a series of records were selected from the next generation attenuation (NGA) West‐2 ground motion database of the Pacific Earthquake Engineering Research center. Then, a high‐rise building in Shanghai, China was modeled using ETABS 20. Through the linear‐elastic time history analysis, the structural seismic response at each floor was computed. Furthermore, human perception thresholds to vibration were introduced to assess the degree of human perception at each floor, illustrating the difference of human perception to seismic tremor at different floors. Finally, a novel earthquake intensity measure (IM), namely average response spectrum intensity (ARSI) within the vibration period ranging from 0.1 s to 10 s was introduced to generate seismic fragility curves for human perception. According to the investigation, it was found that the corner frequency and the associated energy ratio are useful indicators to identify the far‐field long‐period earthquake ground motions. The ARSI is an effective parameter to assess human perception to seismic tremor compared to the spectral acceleration at a given fundamental period. The generated seismic fragility analysis can provide a complete and thorough understanding on the probability of people that should be evacuated under different earthquake intensity levels. The probability of human perception at different floors varies along the building height, demonstrating the difficulty to make crowd evacuation plan in practice. This insight is vital for understanding and mitigating seismic concerns in high‐rise buildings, particularly in low‐to‐moderate seismicity regions.

Comparison of thermal comfort between different heating systems and adaptation to different indoor climates in winter.

Individual heating systems, such as the air-source heat pump (ASHP) air-conditioner or floor heating (FH), are usually used by people living in the hot summer and cold winter (HSCW) zone of China to heat indoor climates in the winter. However, little research has been conducted in the HSCW zone on the thermal comfort difference between indoor climates heated by ASHP air-conditioners and those heated by floor heating, as well as how occupants adapt to different indoor climates. We conducted a comparative field experiment in ASHP-heated and FH-heated apartments in Nanjing to investigate how different types of heating systems influence the thermal sensation of occupants, and we conducted a comparative field experiment in ASHP-heated office buildings and naturally ventilated teaching buildings in Shanghai to investigate how occupants adapt to different indoor thermal environments. Indoor environmental parameters and body surface temperatures were measured using instruments, and occupants' thermal sensation, activity level, and clothing were evaluated using the questionnaire. The results show that floor heating improves thermal comfort by raising foot temperature compared to the ASHP air-conditioner, and that occupants become acclimatized to different indoor climates by adjusting neutral operative temperature. According to the findings, there is no need to overheat the indoor environment in the HSCW zone because occupants can adapt to their experienced thermal environment and it is critical to maintain warm foot temperature in the cool/cold indoor environment.

Optimization of the thermal-optical performance of a PCM-integrated thermochromic glazing system

Facades that employ adaptive technologies have shown great potential in enhancing building energy efficiency. This study investigates a novel phase-change material integrated thermochromic glazing (PCM-TCG), and applies it on skylights of an exhibition building in Shanghai. Thermal performance and daylighting performance of the building were analysed using Fluent and Grasshopper/Honeybee respectively and they were optimized through Non-dominated Sorting Genetic Algorithm-II. It was found that on summer solstice and winter solstice, the optimized solution provided an excellent trade-off between thermal and daylighting performance, reducing the total energy demand by 88% and increasing the daylighting satisfaction time by 5 hours compared to low-E glazing on summer solstice and winter solstice.

The efficacy of building information model in cost control of green buildings in Shanghai

With the rapid development of modern cities, green buildings have become an important way to reduce energy consumption and environmental impact. The focus of the industry has shifted towards controlling the costs associated with these green buildings. As a solution, Building Information Modelling (BIM), an integrated digital tool, has gained widespread usage in the cost control of green buildings, including in the city of Shanghai. As a centre city of China, Shanghai has both prospects and challenges in the field of green building cost control, it is extremely important to conduct this comprehensive study in Shanghai. Therefore, the primary objective of this study is to delve into the impact of BIM on the cost control of green buildings in Shanghai and unveil its effect during the implementation process of green building projects. To achieve this goal, the author used qualitative research and conducted in-depth interviews with engineers who have used BIM technology in various green building projects. The interviews explored the application of BIM and its effect on controlling costs. The findings of this study reveal that BIM has had a significantly positive impact on the cost control of green buildings in Shanghai. By implementing BIM, the efficiency of cost control has been enhanced throughout the entire life cycle of these environmentally friendly structures. This improvement stems from the ability of BIM to provide accurate and detailed information about the building's design, construction, and operation, enabling better decision-making and resource allocation. The implications of this study are twofold. First, it helps to theoretically understand the impact of BIM on green building cost control, especially in the context of Shanghai. This adds to the existing body of knowledge and furthers research in the field. Secondly, this study also fills the research gap of BIM in the cost control of green building projects in Shanghai.

A novel IoT-based framework with Prognostics and Health Management and short term fire risk assessment in smart firefighting system

In recent years, “smart firefighting” becomes a popular research topic which aims at improving the efficiency of fire protection and safety. However, in existing smart firefighting systems: (1) state and analog firefighting IoT data is not fully utilized; (2) fire risk assessment requires a long time to update; (3) few existing works can recognize faults and forecast potential risks with both temporal and spatial information from multiple IoT sensors. Therefore, a novel framework is proposed to solve the above problems as follows: (1) Short-term Fire Risk Assessment (SFRA) is proposed by considering the working status, maintenance and rectification performance of firefighting facilities based on IoT state data; (2) Prognostics and Health Management (PHM) algorithms are applied in the fire safety field for the first time to detect faults and forecast potential risks with temporal and spatial information with IoT analog data from single or multiple sensors. A case study is carried out in a building in Shanghai and a smart firefighting system is deployed with the edge computing gateway. The experimental results of the SFRA and PHM are presented and analyzed in details. The performance evaluation and comparison results are presented with multiple metrics. The reliability of the proposed framework is discussed. The case study concluded that the proposed framework extends the utilization of IoT data, improves the efficiency of fire risk assessment and enhances the accuracy of faults detection and potential risk forecast. Moreover, the deployed firefighting system improves the data privacy and computational efficiency.

Predicting energy use in construction using Extreme Gradient Boosting.

Annual increases in global energy consumption are an unavoidable consequence of a growing global economy and population. Among different sectors, the construction industry consumes an average of 20.1% of the world's total energy. Therefore, exploring methods for estimating the amount of energy used is critical. There are several approaches that have been developed to address this issue. The proposed methods are expected to contribute to energy savings as well as reduce the risks of global warming. There are diverse types of computational approaches to predicting energy use. These existing approaches belong to the statistics-based, engineering-based, and machine learning-based categories. Machine learning-based frameworks showed better performance compared to these other approaches. In our study, we proposed using Extreme Gradient Boosting (XGB), a tree-based ensemble learning algorithm, to tackle the issue. We used a dataset containing energy consumption hourly recorded in an office building in Shanghai, China, from January 1, 2015, to December 31, 2016. The experimental results demonstrated that the XGB model developed using both historical and date features worked better than those developed using only one type of feature. The best-performing model achieved RMSE and MAPE values of 109.00 and 0.24, respectively.

Discussion on the Analysis of Fire Hazard in the Central Hall of a Historical Building

This study focuses on the inadequate compliance of natural smoke exhaust windows in the central hall of a historical building in Shanghai. Taking into consideration the preservation of the authenticity of the historical structure, five fire scenarios were established to evaluate the fire hazard in the central hall based on different fire heat release rates, fire growth coefficients, number of open windows and window response times. Considering the actual conditions of the historical building and its function as a medical facility, the study also analyzed the rationality of setting fire scenario parameters for evacuation time and fire safety assessment. The research findings indicate that the upper smoke temperature is significantly influenced by the values of heat release rate and the number of open windows. Increasing the number of open windows by 60% can extend the time for the upper smoke temperature to reach a dangerous state by approximately 10%.

Repair of damp-proof layers of historic masonry buildings in Shanghai, China

Rising damp is a common problem in historic masonry buildings worldwide. This paper describes a study of two methods currently in use for repairing damp-proof layers in such buildings in Shanghai, China: mechanical physical methods and chemical injection methods. It provides two case studies to demonstrate the use of each method in practice. The aim of the paper is to aid in the selection of suitable and reliable protection and repair technologies of damp-proof layers, to standardise construction technology and to ensure the continued and sustainable use of historic buildings.

Stochastic planning for low-carbon building integrated energy system considering electric-heat-V2G coupling

The low-carbon building has been proposed to mitigate the climate change caused by environmental problems and realize carbon neutrality in urban areas. In addition, the integrated energy system (IES) has been developed to reduce renewable energy curtailment in the power distribution system and improve energy efficiency due to the independent operation of traditional energy systems. In this paper, we propose a stochastic planning method for low-carbon building IES, in which the Vehicle to Grid (V2G) is also considered to further increase the flexibility of low-carbon buildings. The proposed planning method optimizes the investment and operation costs, and CO2 emission of the building IES, to achieve the maximum benefit of the low-carbon building and help realize carbon neutrality. By considering the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, a two-stage stochastic programming model is formulated with chance constraints, in which the heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) methods are applied to deal with the uncertainties. In the case study, a real IES office building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as the test system to verify the effectiveness of the proposed planning method, and the functions of the ESS and EV in IES are analyzed in detail in different operation scenarios. The case study results show that the proposed planning scheme can reduce the total cost and carbon emission significantly.© 2017 Elsevier Inc. All rights reserved.

An Electricity Consumption Disaggregation Method for HVAC Terminal Units in Sub-Metered Buildings Based on CART Algorithm

Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively.

High-resolution spatial assessment of the zero energy potential of buildings with photovoltaic systems at the city level

Zero energy buildings are considered as a viable way to reduce energy use and CO2 emissions in the building sector. This study proposes a high-resolution assessment framework by combining top-down and bottom-up approaches to evaluate the zero energy potential of buildings with photovoltaic systems at the city level. We selected Shanghai as a case study to validate the practicality and feasibility of the proposed framework. Four energy scenarios were further designed based on the location of the installed solar photovoltaic panels (i.e., building roofs and facades) and the window-to-wall ratio. The results show that most buildings in Shanghai's central area show a high potential for zero energy consumption. However, buildings located in Shanghai's suburbs have difficulty in achieving zero energy if only solar photovoltaic systems are used. In summary, the substitution rate (i.e., the ratio of the coal equivalent of solar energy potential to building energy consumption) for the four scenarios ranges from 25.74% to 53.13%. This indicates that the application of solar energy received by buildings can reduce more than half of Shanghai's building energy consumption during the operation phase. The proposed framework may assist policymakers in developing better energy conservation strategies and improving solar energy utilization in buildings.

A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption

Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R2 between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications.