IoT-based BIM integrated model for energy and water management in smart homes

Increasing urbanization and growth in infrastructure create a demand to utilize modern tools to manage human needs. Effective integration of the Internet of Things (IoT) into the design of smart homes is an actively growing area in the construction industry. The ever-increasing demand and cost of energy require a smart solution in the design stage by the construction industry. It is possible to reduce household energy consumption by utilizing energy-efficient sustainable materials in infrastructure construction. Building Information Modeling (BIM) can provide a solution to effectively manage energy. The integration of the IoT further improves the design of comfortable smart homes by utilizing natural lighting. BIM aids in determining energy efficiency and making decisions by presenting the user with several design options via the 6D method. The present study considered a sample home design following the National Building Code (NBC) and American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) standards for implementation. Natural lighting analysis is carried out with the tool Insight 360 to analyze the energy consumption of the building. Some of the outputs obtained from the analysis are wall-to-window ratio (WWR), window shades, design options for window glass, energy use intensity (EUI), and annual energy cost (AEC). The results of the outputs are compared to find the energy-efficient optimum natural lighting of the proposed building. The lesser EUI (16%–21%) and AEC (23%–28%) are identified with the utilization of low emissivity glass in window panels compared with other types of glass. The proposed IoT-based BIM integration model proves that the effective utilization of natural lighting reduces overall household energy consumption

Influence of Nano Composites on the Impact Resistance of Concrete at Elevated Temperatures

The addition of nanomaterials to concrete efficiently fills the pores of the concrete, thereby improving its hardening characteristics. However, no research is available in the literature that investigated the influence of nano-cement (NC), nano-silica-fume (NS), nano-fly-ash (NF), and nano-metakaolin (NM), which are used as partial replacements for cement, on the impact strength (IS) of concrete at elevated temperatures. This issue is addressed herein. Nanomaterials were used in this study to replace 10%, 20%, and 30% of the cement in four different grades of concrete, starting from M20 to M50, at different temperatures. This nano-blended matrix was exposed to various temperatures ranging from 250 °C to 1000 °C, with an increment of 250 °C. In total, the results of 384 new tests were reported. In addition, morphological changes undergone by the concrete specimens were observed through a scanning electron microscope (SEM). The study revealed that the type of binder, proportion of binder, heating intensity, duration, and cooling type directly influenced the impact strength of concrete when subjected to elevated temperature. In comparison to NC, NF, NS, and NM, the mix with NC possessed superior performance when it was heated at 1000 °C. Prior to being subjected to elevated temperatures, the MK blended concrete mix performed well; however, when subjected to elevated temperatures, the MK blended concrete also experienced severe damage

Shrinkage Crack Detection in Expansive Soil using Deep Convolutional Neural Network and Transfer Learning

The formation of shrinkage cracks is a natural phenomenon in expansive soils. The development of these cracks affects both the physical and mechanical properties of the soil. This paper proposes new procedures for predicting and detecting the formation of crack patterns in expansive soils, based on customized Convolution Neural Network (CNN) and transfer learning. A total of four different deep learning models are developed to detect the soil crack pattern by changing the convolution layers and hyper-parameters in the analysis. The novelty of the proposed detection methods lies in the use of customized CNN models in shrinkage crack detection for expansive soils. The customized CNN models are constructed by varying the number of convolution layers and the hyperparameters. The results show that the proposed CNN models provide very accurate results and are capable of detecting the presence of cracks in the soil with great accuracy. The best results are from one of the customized CNN models namely the Customized CNN Model 2 which consists of five convolution layers, three activation layers, one pooling layer, two fully connected layers, and a softmax layer. The results from this model are compared with other well-known approaches from the literature and are shown to provide improved results. Overall, the proposed deep learning methods developed in this paper produce excellent results in terms of the accurate detection of shrinkage soil cracks and can also be applied to other types of soil cracks

Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete

The present research investigates the behaviour of sustainable Self-Compacting Concrete (SCC) when subjected to high temperatures, focusing on workability, post-fire impact resistance, and the effects of fire protection coatings. To develop environmentally friendly SCC mixes, Supplementary Cementitious Materials (SCM) such as Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBFS), and Expanded Perlite Aggregate (EPA) were used. Fifty-six cubes and ninety-six impact SCC specimens were cast and cured for testing. Fire-resistant Cement Perlite Plaster (CPP) coatings were applied to the protected specimens, a passive protection coating rarely studied. SCC (unprotected and protected) specimens, i.e., protected and unprotected samples, were heated following the ISO standard fire curve. An extensive comparative study has been conducted on utilising different SCMs for developing SCC. Workability behaviour, post-fire impact resistance, and the influence of fire protection coatings on sustainable SCC subjected to high temperatures are the significant parameters examined in the present research, including physical observations and failure patterns. The test results noted that after 30 min of exposure, the unprotected specimen exhibited a significant decrease in failure impact energy, ranging from 80% to 90%. Furthermore, as the heating duration increased, there was a gradual rise in the loss of failure impact energy. However, when considering the protected CPP specimens, it was observed that they effectively mitigated the loss of strength when subjected to elevated temperature. Therefore, the findings of this research may have practical implications for the construction industry and contribute to the development of sustainable and fire-resistant SCC materials

Smart farming application using knowledge embedded-graph convolutional neural network (KEGCNN) for banana quality detection

The appearance of fruits is crucial in their quality grading and consumer choices. Colour, texture, size, and shape determine fruit quality. Existing computer vision systems have been implemented for external quality control, relying on observations for fruit grading and classification. Banana quality detection systems, which employ advanced algorithms and sensors to evaluate the ripeness and general quality of bananas throughout their life cycle, are an innovative application of smart farming technology. In this proposed system, Knowledge Embedded-Graph Convolutional Neural Networks (KEGCNNs) are employed to classify and grade banana fruit. The approach aims to detect banana fruit quality by converting banana images into a knowledge graph, applying knowledge embedding to transform them into a continuous vector space, and using Graph Convolution Neural Networks (GCNNs) to analyze the graph structure and make accurate detections. KEGCNNs are especially useful for detecting the quality of banana fruits because they provide a form for capturing the contextual interactions between distinct nodes. KEGCNNs can learn from the data within the graph in an unsupervised manner, allowing them to use the knowledge inherent in the graph structure. KEGCNNs enable more accurate and efficient diagnosis of banana quality as they can discover patterns in data that conventional machine learning algorithms cannot. The suggested technique demonstrates an impressive performance score, indicating its suitability for detecting the quality or grade of banana fruit

Influence of Heating–Cooling Regime on the Engineering Properties of Structural Concrete Subjected to Elevated Temperature

Structural concrete has become a highly preferable building material in the construction industry due to its versatile characteristics, such as workability, strength, and durability. When concrete structures are exposed to fire, the mechanical properties of concrete degrade significantly. The research on the residual mechanical properties of concrete after exposure is necessary, particularly for the repair and rehabilitation of concrete elements and for the stability of the infrastructure. Factors, such as the grade of concrete, the effect of temperature exposure, and rapid water cooling, affect the residual strength characteristics of concrete. Considering these factors, the present investigation evaluates the mechanical properties of concrete using different grades, such as those ranging from 20 to 50 MPa, with an increment of 10 MPa. The specimens were exposed to different durations of fire from 15 to 240 min, following the standard rate of heating. A loss of strength was observed after fire exposure for all the grades of concrete. The rate of reduction in tensile and flexural strengths of the concrete was greater than that of compressive strength. The experimental results also showed that the strength reduction is greater for M50 than M20 concrete concerning the duration of heating. A microstructure evaluation confirmed the extent of damage to concrete under varied temperature conditions