A prototype of knowledge-based decision support system for construction cost management

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.

This work is concerned with introducing new methods and techniques for the projects management in construction industry. This research suggest that the Iraqi engineering staff, who work in construction sector, have a poor knowledge about the subject of Decision Support System (D.S.S.) in spite of that this subject have a wide uses in construction companies in the world. So the researcher presents this study to introduce and to increase the knowledge about the concept of Decision Support System. This research consist of the questionnaire process for construction companies, then the research use the questionnaire results in building a proposed Decision Support System, also the questionnaire process indicate the relation between the decision structure and the organizational levels. The results obtained from the questionnaire process shows that there is a requirement for Decision Support System in cost control decisions support. So the researcher build a Decision Support System for cost control process which can be used by the planner and estimator for different types of projects. The researcher also applies and evaluates the proposed system in some Iraqi construction companies. The application and evaluation process recommended the needs for applying the Decision Support System for project management in construction companies.

The goal of this study is to develop a prototype UI/UX Decision Support System for Determining Lecturer Research Linearity based on user needs, in which users can manage research data, determine the suitability of research conducted by lecturers with their scientific fields, provide a basis for decision-making in determining whether lecturer research should be continued, and provide an overview to decision-makers regarding lecturer performance. This study used the design thinking technique, which is divided into five stages: obtaining information from users, analyzing user needs, generating ideas for solutions to user problems, creating prototypes to solve user problems, and testing and improving prototypes. This study successfully created a decision support system design and prototype that is centered on user demands and comfort. Prototype testing was carried out by UI/UX design experts. The test findings demonstrate that users respond positively to the decision support system interface and that most functionalities work properly. This demonstrates that the decision support system prototype was created with user demands, functional suitability, and convenience of use in mind. The findings of this study are expected to be used in associated institutions or agencies to collect feedback for future system improvement.

The study is an assessment of time, cost and quality management in the Nigerian construction industry, and it aims to explore time cost and quality management in the construction industry. The objective of the study is to identify factors affecting time; cost and quality management in building construction projects. This study is limited to the view of a convenient sample of professionals in the building construction industry. The method used to collect data were questionnaire and interview while the data collected were subjected to descriptive statistical analysis using percentage, mean score and frequency. It was observed that planning and scheduling deficiencies, fraudulent practice and kickback and absence of clear Evaluation standards are the major factors affecting time, cost and quality in construction project. The study recommends that construction team should strive to ensure effective management of time, cost and quality in building production.

Decision support systems under multiple objectives (MODSS) has been classified as a specific type of system within the broad family of DSS and has been one of the most active areas of research. However, despite its theoretical development large scale real-world applications of multiobjective decision-making (MODM) methods had been seriously lacking due to the technical knowledge and expertise needed to select and apply the most appropriate method. This paper explores the possibility of embedding intelligent guidance within MODSS, and outlines a specific guidance framework for the design of knowledge-based guidance for the selection of a suitable MODM method. This framework has been implemented as an intelligent and graphical user interface based multiple objective decision support system prototype. The prototype, with its database, methodology base of MODM and knowledge based system, will help any decision-maker in selecting the most appropriate method in solving various MODM problems.

This study presents the development and application of a prototype decision support system (DSS) for tree selection specifically for Punjab, India, a region facing challenges of low forest cover and an increasing demand for sustainable land use practices. The DSS developed using the R Shiny framework integrates ecological, social, and agro-commercial criteria to facilitate scientific knowledge decision making in tree plantation. The modules in this DSS include a tree selection tool based on comprehensive species attributes, a GIS-based tree suitability map module utilizing an Analytical Hierarchical Process (AHP), and a silviculture practice information module sourced from authoritative databases. Combining sophisticated statistical and spatial analysis, such as NMDS and AHP-GIS, this DSS mitigates data redundancy in SDM while incorporating extensive bibliographic research in dataset processing. The study highlights the necessity of fundamental niche-based suitability in comparison to realized niche suitability. It emphasizes on the importance of addressing ecosystem services, agro-commercial aspects, and enhancing silvicultural knowledge. Additionally, the study underscores the significance of local stakeholder engagement in tree selection, particularly involving farmers and other growers, to ensure community involvement and support. The DSS supports agroforestry initiatives and finds applications in urban tree management and governmental programs, emphasizing the use of scientific literature at each step, in contrast to relying solely on local knowledge.

Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

Anticipating the establishment of pest reservoirs, and therefore pest infestation in sugarcane agrosystems, is a challenge for the implementation of integrated pest management (IPM) programs. The objective of this work was to develop a decision support system that suggests host plant species and pest natural enemies located in a production area. A knowledge base system (KBBI) was developed and coupled to DECIPESTS, a decision support system for PEST management in sugarcane. According to an observed damage, KBBI suggests the wild and cultivated plants that host the potential pests previously identified by DECIPESTS. The comparison with a local floristic inventory enables to determine pest reservoirs. Applied to a case study in Senegal, the system showed, for instance, that Eldana saccharina can be hosted by nine wild plant species located in the irrigation canals and two neighboring cultivated crops of socioeconomic importance in the area. This latter result indicates that the management of Eldana saccharina has to be tackled jointly by local farmers to be successful. While DECIPESTS uses a tactical approach to identify possible causes of pest infestation, its combination with KBBI makes it a strategic tool to enhance IPM strategy at a local scale.

Integrated knowledge management model and system for construction projects

Within the framework of a technical cooperation project between ACSAD and BGR a Decision Support System (DSS) for water resources management was developed and applied in two pilot areas.

Industrial dairy farms boast highly advanced health monitoring and disease diagnosis systems. But without easily accessible, user-friendly web platforms for real-time decision-making, most dairy farmers cannot proactively manage herd health management and optimize treatments based on disease prediction and prevention. To bridge this gap, we have developed a web application of a Decision support system (DSS) for dairy health management based on machine learning. The system architecture combines a Flask backend with a React frontend and scalable cloud data storage and includes preprocessing, data integration, predictive modelling, and cost analysis. DSS forecasts herd diseases with an accuracy 6.66 mean absolute error and 2.35 median absolute deviation across predictions. Its core predictive capabilities rely on long short-term memory (LSTM) neural networks to forecast disease progression from historical records and on a linear trend model to project cuts in treatment costs. The system calculates medication dosages and cost per disease, streamlines supplier selection, and simulates various treatment scenarios, thereby identifying high-cost diseases with potential savings. In other words, this DSS application processes disease and treatment data by incorporating veterinary records into advanced data analytics and neural networks, thereby predicting diseases, optimizing disease prevention and treatment strategies, and reducing costs. As such, this DSS application provides dairy farmers with a tool for strategic decision-making, veterinary treatment planning, and cost-effective disease management towards improving animal welfare and increasing milk yield.

Water leakage is acknowledged as one of the most important issues that drinking water supply systems are facing worldwide. Non-Revenue Water is estimated to 346 million m3 per day and its cost/value is estimated to 39 billion USD per year. At the same time drinking water quality is jeopardized from the water intake points to the consumer’s tap, even during normal operating conditions.ICT support water utility operators to improve the operational capacity of their water supply system. A smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city will be built in the context of SMASH project. It consists of: (a) IoT system comprising three local control stations, installed in selected parts of the water supply network, monitoring water quantity&quality parameters in real time; (b) the hydraulic simulation model of the water supply system of Paramythia; (c) a virtual sensors system, which will be used for water quality prediction; (d) a Decision Support System (DSS) for leakage detection and optimal management of water supply system parameters in an automated manner.The DSS will detect and locate water leakages within the DMA zone and inform the operators for excessive values in drinking water quality parameters. The DSS will use as inputs the data from the IoT system, will interact with the hydraulic simulation model, and obtain the water quality data from the virtual sensors. All these data will be processed by the DSS logic in the backend subsystem. The IoT and the hydraulic simulation data, based on the digital twin of the water supply system, are used for the calculation of specific performance indicators related to water leakage, such as well-known IWA indicators: water losses, ILI, etc. Calculating the divergences between the PI values observed & the ones representing the optimal operation of the water network without leakages and setting appropriate thresholds, the DSS will detect the leakage, while several different scenarios will run in hydraulic simulation. The frontend subsystem of the DSS will be able to visualize the water distribution network, statistical values of water quantitative & qualitative parameters. It will provide alarms in case of leakage or exceedance of water quality parameters’ values and it will show the leakage location in a map. The architecture of the smart green system, currently under development, is depicted in Fig.1.Figure 1. The DSS for the water parameters management in the water supply systemKeywords: Drinking water; water quality; leakage; virtual sensors; smart system; decision support.Acknowledgement: This work is co-financed by EEA Grants 2014 – 2021 and Greek Public Investments Program.Liemberger, R., & Wyatt, A. (2019). Quantifying the global non-revenue water problem. Water Supply, 19(3), 831-837. Antzoulatos G., Mourtzios C., et al (2020), Making urban water smart: the SMART-WATER solution. Water Science & Technology, 82(12), 2691–2710. Alegre, H., Baptista, et al (2016). Performance indicators for water supply services. 3rd IWA publishing.

Towards an integrated weed management decision support system: A simulation model for weed-crop competition and control

This study developed a user-friendly and simplified Spatial Decision Support System using Geographic Information System (GIS) techniques to provide essential information for road managers. This enables the application of appropriate treatments at the right time, based on the available budget. A Global Positioning System (GPS) device was used to collect coordinate data for designated routes within Akure metropolis. Twenty (20) different roads were selected for the study. Data such as road names, coordinates, distances, and infrastructure or facilities unique to each road were collected through reconnaissance surveys, GPS tools, administrative road maps of the city, and street guides clearly showing Akure’s road network. The data were processed using Microsoft Excel, and classifications of the selected roads were made for easy referencing. Database design and creation were carried out using GIS tools within the ArcGIS environment. Digitization of data, labeling of features, symbology, layout design, hyperlinking, and other spatial features were implemented using ArcGIS applications. Each road was linked to its characteristic information, allowing users to access and update information as needed. The result shows that 95% of the total selected roads lack traffic lights and pedestrian bridge facilities. Forty-five percent of the roads have street light facilities. 5% of the road has traffic light facilities. 75% have no bus shelters. Only 5% of the total selected roads have all the identified road facilities. It was concluded that some of the selected roads were not in good condition. While some road facilities were defective, other roads lacked the necessary road facilities that could aid traffic flow. However, the data and associated attributes of each road can be used to support administrative decisions regarding maintenance.

A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP)