Search Results (5)

During roadway excavation, the presence of roof deterioration zones, such as layered spaces and weak interlayers, significantly affects the stability of the surrounding rock. To achieve timely and effective support for roadways, it is essential to utilize drilling measurement signals obtained during the construction of anchorage holes for the identification and prediction of these deterioration zones. This study systematically investigates the response characteristics of thrust, torque, and Y-direction vibration signals to different combinations of rock layers through theoretical analysis, laboratory experiments, ABAQUS dynamic numerical simulations, and field measurements. The results indicate that these drilling parameters effectively characterize variations in rock structure and strength, with distinct signal features observed particularly in roof deterioration zones. Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. The algorithm demonstrates outstanding performance in both laboratory experiments and field tests, achieving a 100% recognition rate for layered spaces and a 96.6% accuracy for identifying deterioration zones, with high accuracy at lower values of mechanical specific energy (MSE). The proposed method provides significant insights for real-time monitoring and control of roof deterioration zones, enhancing the safety and stability of roadway excavations, and serves as a valuable reference for future research and practical applications. Full article

This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies. Full article

This article aims to analyse the performance of green roof in runoff reduction. A case study has been conducted through a deployed green roof at the custom house quay building in Dublin, Ireland. Modular green roofs have been deployed which have IoT scales associated to it for measuring the effective reduction in runoff. Hydro-meteorological variables such as rainfall, temperature, relative humidity and wind speed values were corresponded to the amount of runoff reduction by means of a regression-based relationship. Comparison of the observed runoff reduction from a modular green roof and that estimated based on the developed regression relationship yielded a R² value of 0.874. Through this research, a pattern was identified which established that longer records and better weather variables data have the potential to improve the performance of the regression model in predicting the amount of runoff reduction corresponding to different rainfall and weather patterns. In general, performance of green roof was found to be highly positively correlated to the amount of rainfall received; however, low correlation between rainfall and the percentage of runoff reduction indicate that saturated soil in green roofs considerably deteriorates the performance in runoff reduction. Overall, this study can help in identification of locations where installation of green roofs can help mitigate floods at a city scale. Full article

In arid climates, the optical properties of plastic-covered greenhouses deteriorate very quickly. To examine the effect of greenhouse shape and orientation on the degradation rate of cover optical properties, four greenhouse prototypes were constructed in two shapes (duo-pitched roof and tunnel), covered with a 200 µm thick low-density polyethylene film. Two types were oriented in the North–South direction, the other two in the East–West direction, and all were exposed for one year to an arid climate. Samples were taken from the different surfaces of each cover for testing. The total transmittance (T_s) and reflectance (R_s) of the samples were measured and averaged to obtain the whole cover properties (T and R). Measurements were carried out periodically every 30 days for the four prototypes. The degradation behavior of the optical properties of each cover surface (T_s, R_s) and the whole cover (T, R) was investigated for the four film covers during the exposure time. Results show that the degradation rate of T_s depends on the surface location and the cover orientation. Among the different surfaces of the four prototype covers, the maximum difference in the T_s value between the E and N surfaces for the tunnel cover oriented in the N–S direction was 15.5%. Although the variation of the T_s value among the different cover surfaces was found, the time dependences of the whole cover transmittance (T) for the four covers tested were almost similar. Accordingly, the shape and orientation of the small size greenhouses did not significantly affect the degradation rate of the cover optical properties. In the four covers tested, the reduction in the global solar radiation transmittance (T) was 27–31% after one-year exposure compared to the new film. Nonlinear correlation was developed to predict the degradation rate of the cover transmittance as a function of the accumulated solar irradiance. Full article

Condensation in a building encourages microbial growth, which can have an adverse effect on the health of occupants. Furthermore, it induces the deterioration of the building. To prevent problems caused by condensation, from the design step of a building, predictions of the spatial, temporal and causation for condensation occurrences are necessary. By using TRNSYS simulation coupled with TRNFLOW, condensation assessment of an entire office building in Tokyo, Japan, was conducted throughout the year, including when the air-conditioning system was not operated, by considering the absorption-desorption properties of the building materials and papers in the office and the airflow within the entire building. It was found that most of the condensation occurred during winter and was observed mainly in the non-air-conditioned core parts, especially the topmost floor. Additional analyses, which identified the effect of variations in the thermal insulation of the external walls, roof and windows and the airtightness of the windows on condensation, showed that the lower airtightness of windows resulted in decreased condensation risks, and condensation within the building was suppressed completely when the thermal insulation material thickness of the external walls was greater than 75 mm, that of the roof was greater than 105 mm and the windows had triple float glass. Full article