Keywords

1 Introduction

1.1 Background

The construction industry significantly impacts the environment, consuming 40% of final global energy consumption, and contributing to 39% of total CO2 emissions [1, 2]. In China, it accounts for an even higher percentage of energy consumption (46%) and CO2 emissions (51%) [3, 4]. The energy consumption and CO2 emissions from the operation phase constitute around 80% of a building’s life-cycle total amount [5].

The conventional “on-site” construction method, which refers to the process that a building is constructed on the construction site, does not have an advantage in meeting the demand of fast, low-cost and eco-friendly construction [6]. Among off-site construction methods, Modular integrated Construction (MiC) is categorized into the highest level, which means modules are fabricated in an off-site factory, where all the structural, mechanical, plumbing, electric and decorative work is done (about 85–90%), then delivered to the construction site and assembled to form the building [7].

1.2 Literature Review on MiC’s Environmental Performance

As China has introduced policies to promote the development of MiC, it is necessary to investigate its environmental benefits, so there is some research on the environmental performance of MiC [8]. However, fewer existing papers focus on the operation phase of MiC [9, 10]. Studies present the full life cycle assessment for residential and commercial buildings and found the pre-fabricated steel-framed modular used more operational energy than the conventional concrete building [11, 12]. The thermal performance of a building’s envelope significantly impacts the cooling or heating load that takes up most of the energy use [13]. MiC has great potential to improve its thermal and energy performance because of the higher performance envelope, higher manufacturing quality and precision, easier dismantling and reuse [14].

The primary focus of this research is on investigating the environmental performance of MiC during the operation phase through a case study of a real project and implementing comparative LCA analysis of common prefabricated construction methods and MiC. This paper provides: (1) a systemic method of real-time tests and simulation to evaluate the operational environmental performance of MiC; (2) envelope design guidance of MiC and the resulting quantitative environmental benefits.

2 Experiment

2.1 Case Study

The Longhua Zhangkengjing project is a social housing project in Shenzhen, China. It is the first high-rise concrete and the tallest MiC in China. It consists of five 28-story residential buildings, each with a height of 99.7 m. Also, it is the first MiC built in the whole process of green construction in China and was completed in only one year. A 35 m2 housing unit facing Southwest is selected for the investigation (Fig. 1).

Shenzhen has a warm and humid subtropical climate (Köppen classification Cwa) and belongs to hot summer and warm winter areas based on the thermal design code of civil buildings. The average highest and lowest dry-bulb temperature is around 32 ℃ and 13 ℃, necessitating cooling from May to November while no heating is needed. Overall, it is necessary to allow enough daylight indoors while reducing solar radiation which may increase the cooling load. Therefore, the envelope is designed to reduce the heat gain indoors (Fig. 2).

Fig. 1.
figure 1

Axonometric drawing of the studied 35 m2 housing unit

Full size image
Fig. 2.
figure 2

The construction of the walls of the experimental room

Full size image

2.2 Experiment Setting of the Real-Time Test

The real-time test was conducted from 17 to 22 June 2023, when it was cloudy and rainy. Ten sensors (Hobo MX1104, 20°–70 ℃, ±0.20 ℃ for temperature sensors; 0–167731 lx, ±10% for light sensors) were set on to measure the temperature and the illuminance of each surface. Sensor 6th and 10th were set to measure the indoor and outdoor air temperature separately. The blower door testing was conducted to test the air tightness of the unit (Fig. 3).

Fig. 3.
figure 3

(left) Indoor view when conducting blower door testing; (right) the layout of sensors

Full size image

2.3 Simulation Method

Ladybug Tools, plug-ins on the Rhino/Grasshopper platform with engines such as Energyplus for energy simulation and Daysim for daylighting analysis, were used in this study. Firstly the sky model was built for ray-tracing simulation. Solar radiation, dry bulb temperature, and relative humidity are from the 10th Hobo (114.04°N, 22.70°E) and are validated with the meteorological data of Longhua District from the Meteorological Bureau of Shenzhen Municipality (within ±1 ℃), while other parameters are from the SWEAR epw file. The intensity of infiltration is from the blower door test. The simulation interval is 2 h during June 17th 00:00–22nd 00:00. Table 1 shows the parameter settings.

Table 1. Thermal and optical parameters of the materials
Full size table

3 Calibration and Validation

The measured readings were used to compare with the simulations for the same point, and the average difference was calculated in Table 2, which was within the acceptable range (±1.6 ℃ for temperature and ±16 lx for illuminance except for the 4th point).

Table 2. The differences between the simulated and the measured readings for all sensors.
Full size table

The simulated and measured illuminance fit well while most deviations occur at noon, which may be a result of the difference between the actual global illuminance and the data from the 10th Hobo. The 4th Hobo’s data may be impacted by the aluminum window frame that blocks sunlight coming indoors.

The simulated and measured temperature of the No. 1, 2, 3, 5, 7, and 8 test points are basically identical, and those of the 4th and 9th points are within the acceptable range (±1.2 ℃). The simulated temperature is the average of the entire surface, different from the measured value at a point, contributing to the deviation. The 6th point exhibits the largest deviation, because the simulated value is the average air temperature of the whole room, which shows a larger fluctuation than that of the center point (Fig. 4).

Fig. 4.
figure 4

Analysis via real-time temperature measurement and numerical simulation.

Full size image

4 Result

4.1 Temporal Variations

The illuminance distribution of each surface of the room on 21 June is selected for visualization and analysis in Table 3. As it was cloudy, the interior of the studied room was hardly exposed to direct sunlight, and only a little bit of reflected light came indoors at 14:00 and 18:00 with the peak illuminance occurring at 14:00.

Table 3. Ray-tracing visualization and illuminance gradient map at 06:00, 10:00, 14:00, 18:00
Full size table

During the measurement period, the outdoor ambient temperature was mainly in the range of 25–34 ℃. As shown in Fig. 5a, the indoor temperature at all points shows less fluctuation than outdoor (10th), and only the 4th and 9th points fluctuated more obviously than others. The external wall (3rd Hobo) receives the greatest influence from the outdoor environment.

Figure 5b reflects three exterior wall surface temperatures in the bedroom. The bedroom exterior wall (3rd Hobo) and bathroom exterior wall (9th Hobo) were exposed to direct solar radiation according to the ray-tracing in Table 3, but 3rd Hobo’s temperature fluctuation amplitude is more moderate, and its peak appearance time is lagged behind outdoor temperature and earlier than the bathroom wall (9th Hobo). On June 16th -18th, the bathroom wall (with the 9th point on the inner side) had the lowest indoor temperature and hardly changed with the outdoor temperature, while on the 19th-21st, it began to fluctuate with the outdoor temperature with lag. The reason is the better thermal insulation performance of the insulation infill walls (3rd Hobo) while the better heat storage performance of the heavy shear wall (9th Hobo).

Fig. 5.
figure 5

Variations of temperature from Hobo, (a) above, all sensors, (b) bottom, No. 3, 4, 9

Full size image

5 Comparative LCA Analysis

The carbon emission calculation of the Longhua Zhangkengjing Project in Shenzhen considers five phases: raw material extraction and manufacturing, transportation, construction, building operation and demolition. The formula for calculating the total carbon emissions is as follows.

$$ {\text{Ctotal}} = {\text{Csc}} + {\text{Cys}} + {\text{Cjz}} + {\text{Cyy}} + {\text{Ccc}} $$
(1)

where Ctotal = total carbon emissions (kgCO2);

Csc = carbon emissions from the raw material extraction and manufacturing (kgCO2);

Cys = carbon emissions from the transportation of materials (kgCO2);

Cjz = carbon emissions during the construction (kgCO2);

Cyy = carbon emissions during the operation (kgCO2);

Ccc = carbon emissions during the demolition (kgCO2).

In order to investigate the carbon emission reduction of concrete MiC, a traditional PC prefabricated construction is used for the comparison to explore the difference between the two construction methods. The data of Csc, Cys, Cjz are referenced from Report on the Whole Life Cycle Carbon Emission Measurement of the Longhua Zhangkengjing Housing Project. The Cyy is calculated based on the energy simulation.

5.1 Baseline Model

According to the building energy codes General Code for Energy Efficiency and Renewable Energy in Buildings, the baseline model uses these inputs: 1.5 W/(m2·K) for the outdoor wall, 0.4 W/(m2·K) for the roof and 3 W/(m2·K) for the glass door in hot summer and warm winter B area. The parameters related to occupancy, lighting, electric equipment were also set based on the code above. The calculated end-use intensity (EUI) of the baseline model is 62.7 kWh/ m2. The carbon emission factor of electricity is 0.4512 kgCO2/kWh, which is the average level of Guidelines for the Preparation of Greenhouse Gas Inventories at the Municipal and County (District) Levels in Guangdong Province (for Trial Implementation). The result of Cyy is 28.3 kgCO2/m2. Assuming that the operation period of the project is 50 years, the calculated carbon emission of the baseline model during the operation phase is 1415 kgCO2/m2 and the carbon emission during the whole life cycle is 1996 kgCO2/m2.

5.2 Energy Saving and Carbon Reduction Analysis

MiC reduces carbon emission by 23.47% than the baseline building of PC prefabrication in the construction phase. It is because industrial means are considered to be adopted as far as possible in the early design phase in the top-level design, intelligent and refined management are implemented in the processing and construction, and reducing the generation of on-site waste from the source is prior. As a result, the waste of materials produced was reduced by 80% compared to conventional on-site construction.

The carbon emission intensity of the building is 26.2 kgCO2/m2, and it is 1310 kgCO2/m2 over a 50-year operation period. Because of the improvement of the MiC envelope’s thermal performance, the carbon emission intensity can be reduced by 2.1 kgCO2/m2 compared to the baseline model, resulting in a reduction ratio of 8%. Overall, the whole life cycle carbon emissions of concrete MiC with a fifty-year building operation period is 1883 kgCO2/m2, which is reduced by 6% compared to the baseline model (PC prefabricated construction).

6 Conclusion

This paper provides: (1) a systemic method of real-time tests and simulation to evaluate the operational environmental performance of MiC; (2) envelope design guidance of MiC and the resulting quantitative environmental benefits through a real case study. The studied concrete MiC has the envelope of higher thermal performance, resulting in 8% lower operational energy consumption and carbon emissions compared to the baseline building of the normal prefabrication construction method. The whole life cycle carbon emissions reduction ratio of the concrete MiC case is 6% per unit area. Even so, there is still room for improvement in further investigation of the environmental performance of MiC. For instance, more cases of different programs, floor areas, and locations can be studied through this method.