SMART WATER CONSUMPTION FORECASTING ACROSS CONSTRUCTION PHASES USING MACHINE LEARNING

Abstract

This invention provides a machine-learning model for forecasting water consumption throughout construction phases, offering accurate, phase-specific predictions based on historical data and environmental conditions. The system supports construction firms in optimizing resource use, reducing costs, and promoting sustainability through real-time monitoring and continuous model updates.

Specification

Description:FIELD OF THE INVENTION
This invention relates to the construction and environmental technology sectors, specifically focusing on a machine-learning model for accurately forecasting water consumption across various construction phases. The invention addresses the inefficiencies in water management and environmental sustainability, providing an intelligent, data-driven approach to optimize water resource use in construction projects.
BACKGROUND OF THE INVENTION
The construction sector faces significant challenges in managing water resources effectively, given the high demand for water across various project stages, including concrete mixing, curing, and dust suppression. Inefficient water use results in substantial waste, increased costs, and environmental strain, particularly in water-scarce regions. Traditional water management practices in construction are often reactive, lacking the precision and flexibility needed to adjust water usage based on real-time demands. Moreover, with the growing emphasis on sustainability, construction firms are under pressure to minimize their environmental footprint, including water consumption. Current forecasting methods are inadequate for the dynamic needs of construction projects, where water requirements vary significantly by phase. This invention introduces a machine learning-based system that forecasts water consumption accurately, adapting to each construction phase's unique demands. By integrating data from past projects and real-time environmental variables, this approach enhances resource planning, regulatory compliance, and sustainable practices, addressing a critical gap in traditional water management methods.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention provides a machine-learning model that forecasts water consumption throughout various phases of construction. Utilizing historical data, environmental conditions, and phase-specific requirements, the model delivers real-time, accurate predictions for water usage. This forecasting system supports construction firms in managing resources efficiently, minimizing waste, and reducing costs. The model incorporates a feedback loop for continuous learning and updates, improving prediction accuracy and adaptability. Additionally, the system provides insights into regulatory compliance and helps reduce environmental impact by promoting responsible water use, making it suitable for large-scale and small-scale construction projects alike.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The Smart Water Consumption Forecasting system employs machine learning to provide precise water usage predictions for construction projects. This system consists of data collection, preprocessing, feature selection, model training, and a real-time feedback loop for continuous improvement. The data collection module gathers historical data from past construction projects, including water consumption patterns, phase-specific needs, and environmental variables like temperature, humidity, and wind speed. Additionally, real-time data is collected from ongoing construction sites, feeding the model with up-to-date information to refine predictions.
Data preprocessing is a critical step, involving data cleaning, normalization, and augmentation to ensure consistency and accuracy. Feature selection identifies the most influential variables affecting water usage, such as project phase, construction materials, and environmental factors. This process minimizes computational overhead and enhances model efficiency, enabling the system to deliver accurate predictions swiftly.
The machine learning model employs algorithms such as linear regression, decision trees, and neural networks, trained on a labeled dataset that includes water consumption records and project attributes. The model is structured to recognize patterns in water usage associated with different construction phases-excavation, foundation, framing, and finishing-providing phase-specific forecasts that adjust to the project's progress.
The system features a real-time prediction interface, accessible via a web or mobile application, where construction managers can view forecasted water needs and adjust resource allocation accordingly. A feedback loop is integrated into the system, continuously monitoring actual water usage and comparing it with predicted values. Discrepancies trigger model updates, allowing it to learn and adapt to new patterns or environmental shifts, thus enhancing accuracy over time.
The system's predictive insights support construction firms in maintaining regulatory compliance, optimizing water use, and reducing operational costs. By providing an intelligent and adaptable solution to water management, the system contributes to sustainable construction practices, meeting the industry's growing demand for resource-efficient methods.
, Claims:1. A smart water consumption forecasting system for construction projects, comprising data collection, preprocessing, machine learning model training, and a feedback loop for real-time predictions of water usage across different construction phases.
2. The system as claimed in Claim 1, wherein data preprocessing includes data cleaning, normalization, and feature selection to enhance model accuracy and computational efficiency.
3. The system as claimed in Claim 1, wherein the machine learning model uses algorithms such as linear regression, decision trees, and neural networks to forecast phase-specific water requirements.
4. The system as claimed in Claim 1, wherein real-time prediction outputs are displayed on an interface, allowing construction managers to monitor and adjust water resource allocation dynamically.
5. The system as claimed in Claim 1, wherein a feedback loop continuously compares predicted water usage with actual consumption, updating the model based on ongoing data to improve prediction accuracy.
6. A method for managing water resources in construction as claimed in Claim 1, involving data-driven forecasting and real-time monitoring to optimize water use and support sustainable practices.

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