Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing

Abstract

To identify hygrothermal transfer patterns of exterior walls is a crucial issue in the design, assessment, and construction of buildings. Temperature and relative humidity, as sensor monitoring data, were collected from the outside of the wall to interior bamboo and wood composite sheathing over the year in Huangshan Mountain District, Anhui Province, China. Combining the machine learning method of reservoir computing (RC) with agglomerative hierarchical clustering (AHC), a novel clustering framework was built for better extraction of the characteristics of hygrothermal transfer on the time series data. The experimental results confirmed the hypothesis that the change in the temperature and relative humidity of the outside of the wall (RHT12) dominated the change of the interior sheathing (RHT11). The delay time between two adjacent peaks in temperature was 1 to 2 h, while that in relative humidity was 1 to 4 h from the outside of the wall to interior bamboo and wood composite sheathing. There was no significant difference in temperature peak delay time between April and July. Temperature peak delay time was 50 to 120 min. However, relative humidity peak delay time was 100 to 240 min in April, whereas it was 20 to 120 min in July. The impact formed a relatively linear relationship between outdoor temperature and relative humidity peak delay time. The hygrothermal transfer patterns were characterized effectively by the peak delays. The discovery of the hygrothermal transfer patterns for the bamboo and wood composite walls using the machine learning method will facilitate the development of energy-efficient and durable bamboo and wood composite wall materials and structures.

A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls

“…Each increment of the index corresponds to a 10 min interval, as the data was collected with a logging time of 10 min. Consistent with our prior research findings in [13], outdoor RH (RH12) and temperature (T12) were found to exert a dominant influence on the inwall's RH (RH10-11) and temperature (T10-11). That is, RHT12 has the largest amplitude and dominates the RH and temperature variations inside the wall.…”

“…The building is a modern example of Hui-style residential architecture, constructed entirely of locally sourced bamboo and Chinese fir materials. The building was left in its natural state without heating or air conditioning to study its hygrothermal performance under regional climate conditions [13]. The use of the experimental building provides a unique opportunity to study the behaviors of building materials under natural conditions, without the confounding effects of heating and cooling systems.…”

“…The sensors were connected to Campbell's CR1000 data logger, which stored the collected data for further analysis. Continuing from our previous study [13], this study analyzed the data collected from the sensor set placed on the north wall of the building, positioned 300 mm above the ground, consisting of RHT9, RHT10, RHT11, and RHT12. The locations of these 4 sensors in the field are shown in Figure 1c.…”

“…To address this issue, advanced machine learning methods are required. In our previous study, we analyzed the hygrothermal transfer pattern from the outside of the wall (RHT12) to the interior bamboo and wood composite sheathing (RHT11) based on the peak delay of the daily hygrothermal time series [13]. The results revealed a dominant effect of RHT12 on RHT11, and an inherent time delay during hygrothermal transfer from RHT12 to RHT11.…”

Improving marigold agriculture with an IoT-driven greenhouse irrigation management control system

“…In addition, a microcontroller, a set of environmental sensors, and an irrigation system (including a relay, a solenoid valve, a pump, and a watering sprinkler system) were used to control and monitor the selected parameters. As follows is the communication between the various entities: -A SHT11 sensor gathered real-time environmental data on humidity and temperature [20] from various greenhouse regions. The SHT11 sensor measures humidity and temperature accurately over a wide measurement range and features industry-proven quality and dependability.…”