Rural and semi-urban areas are increasingly experiencing differential pressures, unreliable supply, aging assets in the water distribution system, leakage losses, and lack of data for operational control in their domestic water distribution networks. This paper aims to create a Q1 level research synthesis that combines construction management and the use of machine learning to predict the best use of water in the home for domestic water supply based on the Nidhori village water distribution report. The study utilizes the supplied field data, and secondary data on pipeline geometry, node elevations, hydraulic parameters, water demand, leakage, supply duration and storage capacity and model outputs. There are eight segments of PVC pipe, from 50 mm to 150 mm in diameter and 200 m to 450 m long, ranging from 6 years old to 12 years old. Records of the hydraulic state indicate a decrease in flow (from 13.0L/s at the overhead tank to 7.8L/s at node N8), a decrease in pressure (from 3.0bar to 1.4bar) and a decrease in supply duration (from 3.0 hours per day to 1.5 hours per day). The comparison of the models shows that the model with Linear Regression has the highest predictive ability with R2 = 0.9996, an accuracy of 99.96%, RMSE = 0.1539, MAE = 0.1245 and MAPE = 0.21%. It is shown that the proposed framework can be integrated to enhance reliability, equity and resource efficiency in domestic water networks.
Water scarcity is an imminent threat, particularly for countries like India. Sustainability of food supplies and rural employment hinges on the development of new irrigation technologies. Current irrigation technologies are not equipped to meet the varied needs of different terrains and employ practices like over-pumping, using too much energy, and distributing water inefficiently. Challenges for transporting water to high elevation areas using lift irrigation technologies are exacerbated. Construction management and AI technologies can help transform lift irrigation technologies to make them energy adaptive and efficient. This study proposes an AI-based lift irrigation technology water distribution model. The model integrates construction management concepts of Critical Path Method (CPM), Value Engineering (VE), and Artificial Intelligence (AI) to optimize the use of IoT, water, and reinforcement learning (RL) to achieve efficient hydraulic transportation. The model helps to optimize the irrigation system by simulating the scheduling of pumps and controlling the valves and channels, and helps to reduce the energy consumed and improve even distribution of water. The model was developed using primary data in the form of surveys, sensor data, and operational logs, and secondary data from government records and weather stations. The model significantly improved pump operations and reduced water loss. The system improved the reliability of the system and reduced energy to operate and hence reduced the overall operational costs. The system incorporates sustainable practices like resource allocation and predictive maintenance to ensure sustainability. The system offers a novel, data-centric, and intelligent approach to managing lift irrigation to address the challenges of water scarcity in agriculture.
water canals or water flood or pump-based irrigation systems generally involve no predictive scheduling or project level cost control, and lacks real-time system feedback. This paper transforms the systematic paper given by the writer to a level of Q1 research paper by synthesizing the Fourth Industrial Revolution (4IR) technologies along with construction management techniques for Kolhapur district, Maharashtra. It highlights the tools for the 4IR, like Internet of Things Technology (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, Drones, Robotics and Blockchain and Digital Twins and how they can make the implementation more reliable using the construction management tools, such as Work Breakdown Structure (WBS), Critical Path Method (CPM), Earned Value Management (EVM), BIM-GIS integration and life-cycle cost analysis with value engineering. The review data set underwent PRISMA-standard screening (1045 database records were identified, 86 records were screened for duplication, 998 records were screened, 276 records were assessed for eligibility, and 95 studies were included). Analysis reveals the potential to save 25-40% water through monitoring using IoT techniques, to improve irrigation decision making using AI in the irrigation scheduling day, and to save 10-24% during the construction phase – in activities such as survey, procurement, installation, monitoring and maintenance. The novelty of this paper is the development of a 4IR-construction-management integration framework for Kolhapur, rather than merely considering smart irrigation as a problem of connecting the sensors with the automation system(s). The outputs are expected to be an implementation-ready readyness model, a technology-priority-matrix for the Dhamni Medium Project and concrete implementation recommendations for the phases of the smart irrigation deployment. The study found that by combining Digital Sensing with Structured Project Planning and Stakeholder Based Management, the irrigation efficiency in Kolhapur can be improved, risk of cost overrun can be reduced, and water governance in the area can be improved over the course of time.
Designing the surface infrastructure for a cyclic CO₂ injection pilot — the spatial arrangement of the CO₂ source connection, sweetening unit, compression and heating skid, and wellhead — has traditionally relied on two-dimensional schematics and process flow diagrams that, while useful for conveying logical connectivity between equipment, do not capture the true spatial layout, routing, and clash-avoidance considerations that determine whether a proposed layout can actually be constructed as drawn. This article examines the role of three-dimensional visualization and conceptual facility design in planning cyclic CO₂ injection infrastructure for mature gas-condensate wells, drawing on the broader oilfield surface-facility visualization literature to outline visualization requirements specific to small-footprint, pilot-scale injection projects, and to propose a practical, cost-conscious approach appropriate for independent operators who do not have access to the enterprise-scale visualization platforms used in major field-development projects.
Moving an iOS application from UIKit to SwiftUI while an established user base keeps using it is incremental by necessity, yet published work classifies such transitions by repository configuration, whole strategy or learned file order, and no prior classification that selects the next screen by its properties was identified in the reviewed literature. This review combines 27 sources, 12 of them read in full text, with the author’s own screen-by-screen migration of a consumer application. The classification has two levels. The first level distinguishes five principles for choosing the next unit: order-driven, component-first, new-code-first, history-learned order and property-driven screen selection. The second level, under the last principle, defines four screen profiles from three properties (isolation, a bounded set of states and coupling to legacy UIKit navigation): leaf, component-dependent, state-heavy and navigation-bound screens. Each profile receives a migration route, a borrowed coexistence seam, a dominant risk and the checks it is most likely to fail. Readiness is defined by six checks instead of visual equivalence: navigation; loading, error, empty and content states; device sizes; return from the background; analytics; and absence of regressions against the UIKit version. Partial rollback of a single screen is modelled as a planned state of the migration lifecycle. The classification gives teams a reproducible criterion for selecting, releasing and returning screens.
