Energy Inputs in Motor-Manual Release Cutting of Broadleaved Forests: Results of Twelve Options

Extensive focus on operational energy research has positively impacted both academia and policymakers, facilitating new strategies that reduce the energy consumed by building occupants. Much less emphasis has, however, been given to embodied energy. Consequently, although studies now show that embodied energy can be responsible for up to 50% of a building’s life cycle energy, little is known about the embodied energy associated with the construction of buildings, materials, and components in the study context. The aim of this study is to investigate the current scenario in the United Arab Emirates (UAE) by calculating the embodied energy of a residential villa, and estimating the initial, recurrent, and demolition and disposal embodied energies over a 50-year building life span. A detailed assessment of the embodied energy associated with the construction of the case study villa was carried out using an input–output hybrid approach, followed by a sensitivity analysis focused on variations related to the energy associated and consumed, as well as the adoption of renewable energy sources. The findings show that the initial embodied energy was 57% of the life cycle embodied energy and 19% of the life cycle energy of the villa while the recurrent embodied energy was 43% of the life cycle embodied energy and 14% of the life cycle energy of the villa. The life cycle embodied energy of the villa, over a 50-year life span was 36% of the life cycle energy. This paper also highlights the impact of adding a solar PV system and lists multiple areas for future studies related to embodied energy and its benefit to stakeholders in the building industry.

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Development of a Modality-Invariant Multi-Layer Perceptron to Predict Operational Events in Motor-Manual Willow Felling Operations

Borz

2021

Forests

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Motor-manual operations are commonly implemented in the traditional and short rotation forestry. Deep knowledge of their performance is needed for various strategic, tactical and operational decisions that rely on large amounts of data. To overcome the limitations of traditional analytical methods, Artificial Intelligence (AI) has been lately used to deal with various types of signals and problems to be solved. However, the reliability of AI models depends largely on the quality of the signals and on the sensing modalities used. Multimodal sensing was found to be suitable in developing AI models able to learn time and location-related data dependencies. For many reasons, such as the uncertainty of preserving the sensing location and the inter- and intra-variability of operational conditions and work behavior, the approach is particularly useful for monitoring motor-manual operations. The main aim of this study was to check if the use of acceleration data sensed at two locations on a brush cutter could provide a robust AI model characterized by invariance to data sensing location. As such, a Multi-Layer Perceptron (MLP) with backpropagation was developed and used to learn and classify operational events from bimodally-collected acceleration data. The data needed for training and testing was collected in the central part of Romania. Data collection modalities were treated by fusion in the training dataset, then four single-modality testing datasets were used to check the performance of the model on a binary classification problem. Fine tuning of the regularization parameters (α term) has led to acceptable testing and generalization errors of the model measured as the binary cross-entropy (log loss). Irrespective of the hyperparameters’ tunning strategy, the classification accuracy (CA) was found to be very high, in many cases approaching 100%. However, the best models were those characterized by α set at 0.0001 and 0.1, for which the CA in the test datasets ranged from 99.1% to 99.9% and from 99.5% to 99.9%, respectively. Hence, data fusion in the training set was found to be a good strategy to build a robust model, able to deal with data collected by single modalities. As such, the developed MLP model not only removes the problem of sensor placement in such applications, but also automatically classifies the events in the time domain, enabling the integration of data collection, handling and analysis in a simple less resource-demanding workflow, and making it a feasible alternative to the traditional approach to the problem.

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Energy Inputs in Motor-Manual Release Cutting of Broadleaved Forests: Results of Twelve Options

Cited by 4 publications

References 25 publications

Embodied water-energy nexus towards sustainable building construction: Case of conventional Indian houses

Embodied water-energy nexus towards sustainable building construction: Case of conventional Indian houses

Embodied and Operational Energy of a Case Study Villa in UAE with Sensitivity Analysis

Development of a Modality-Invariant Multi-Layer Perceptron to Predict Operational Events in Motor-Manual Willow Felling Operations

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