Data-Driven Process Mining Framework for Risk Management in Construction Projects

Construction Projects are exposed to numerous risks due to their complex and uncertain nature, threatening the realization of the project objectives. However, Risk Management (RM) is a less efficient realm in the industry than other knowledge areas given the manual and time-consuming nature of its processes and reliance on experience-based subjective judgments. This research proposes a Process Mining-based framework for detecting, monitoring, and analysing risks, improving the RM processes using evidence-based event logs, such as Risk Registers and Change-Logs within previous projects' documents. Process Mining (PM) is a data- driven methodology, well established in other industries, that benefits from Artificial Intelligence(AI) to identify trends and complex patterns among event logs. It performs well while intaking large amounts of data and predicting future outputs based on historical data. Therefore, this research proposes a Bayesian Network (BN)-based Process Mining framework for graphical representation of the RM processes, intaking the conditional dependence structure between Risk variables, and continuous and automated risk identification and management. A systematic literature review on RM, PM, and AI forms the framework theoretical basis and delineates the integration areas for practical implementation. The proposed framework is applied to a small database of 20 projects as the case study, the scope of which can be tailored to the enterprise requirements. It contributes to creating a holistic theoretical foundation and practical workflow applicable to construction projects and filling the knowledge gap in inefficient and discrete conventional RM methods, which ignore the interdependencies between risk variables and assess each risk isolated

Data-drive decision support system for selecting building retrofit strategies

The building sector in EU countries is primarily comprised of outdated and inefficient structures, which are of high energy consumption and seismic vulnerability. As a result, building retrofit is being stressed as a viable option for addressing existing energy and seismic issues in the construction industry, particularly in residential properties. For this purpose, strategic decisions should be made about the retrofit strategies, which require great time, effort, resources, and expertise. While traditional case-based retrofit scenarios fail to provide rapid and objective solutions, data-driven methods such as Artificial Intelligence (AI) technologies can serve as an effective and efficient decision support system for selecting retrofit strategies. This research offers a clustering of residential properties in the CENED database (Lombardia 2007)(comprising over 1 million energy labels of residential properties), based on the construction year and U-value. These clusters are associated with the type of material and building technique using the National scientific report on the TABULA activities (Corrado, Ballarini, and Corgnati 2012), and the probability distribution of EHP values. Therefore considering a given U-value and an energy class, the most optimum retrofit strategy is suggested to obtain a particular energy label. This research benefits from AI technologies to enhance strategic decision-making for building retrofit by connecting the current dispersed databases. It also helps increase energy-saving on an urban level

THE EFFECT OF WASTE MARBLE POWDER AND SILICA FUME ON THE MECHANICAL, ENVIRONMENTAL AND ECONOMIC PERFORMANCE OF CONCRETE

One of the most widely used materials in the construction industry is concrete and one of its main constituent elements is cement. Cement production, in addition to consuming non-renewable natural resources, emits greenhouse gases and pollutes the environment. Waste marble powder (WMP) is an industrial by-product that the marble factory generates considerable amounts during stone processing. WMP mostly not used in the industry nor being recycled and causes serious environmental problems. Silica fume (SF), also known as micro silica, is an ultrafine powder collected as a by-product of the silicon and ferrosilicon alloy production. This paper presents the results of an experimental investigation of mechanical properties carried out on the concrete mixes containing WMP and SF as a partial replacement of Portland Cement (PC). In all concrete mixtures, a constant water/binder ratio was used and concrete mixtures with a target initial slump of 80±10 were prepared. All of the concrete mixtures were assessed in terms of environmental, economic and mechanical aspects. Global warming potential, fossil fuel depletion potential and acidification potential were considered as environmental indicators of concrete mixtures production. Finally, it was observed that the mechanical properties of concrete containing WMP are improved for replacement ratios up to 10%. The use of SF improved the performance of all mixing designs and compensated for the shortcomings in the mechanical properties of concrete containing high percentages of WMP. Mechanically, the best percentages for simultaneous use of WMP and SF are 5% and 10%, respectively. From an environmental point of view, replacing 20% of WMP and 10% of SF with PC, not only leads to the production of concrete with suitable mechanical properties but also reduces the use of cement by 30% and the resulting environmental pollution. The combined index designed in this study showed that the optimal mix design in terms of mechanical, environmental and economic belongs to a mixture in which 5% WMP and 5% SF are used simultaneously

Data-driven decision support system for building stocks energy retrofit policy

In most European countries, residential assets account for as much as 85% of the building stock floor area and are, on average, very outdated and energy inefficient. Moreover, the European Commission published the EU Green Deal invigorating higher retrofit of private and public buildings. Nowadays, public authorities collect extensive datasets to analyze the existing building stock; however, the complex and diverse scenario makes the definition of retrofit policies cumbersome. The biggest hurdle is often linked to the high cost of acquiring information. The presented research tries to overcome these issues by introducing a decision support system for retrofit policymaking from low-cost data-driven approaches. The method is based on: i) clustering techniques to divide building assets into groups with similar characteristics and energy consumption, and ii) Montecarlo simulation to compute each cluster's energy savings based on different retrofit scenarios. The proposed method has been successfully applied to an extensive portfolio of residential assets in Lombardy Region in Italy, called the CENED database, with over one million assets. As a result, the introduced method defines the optimum retrofit scenario with a low cost of information (e.g., without expensive surveys to gather data on existing assets' characteristics and performance indicators) and determines the number of assets to be retrofitted along with the expected energy savings. This data-driven approach can be easily updated given new renovations and status changes in the built environment, making it useable for the long term or in different regions. To summarize, data-driven solutions are now required to accomplish the European Union's decarbonization ambitions, and the proposed method helps decision-makers choose better energy retrofit policies for the built environment

Monitoring pollution to protect vulnerable people in outdoor environment

Slow Onset Disasters (SLODs), especially air pollution, have a severe negative effect on residents’ lives, especially the vulnerable groups. Therefore, constant measurement of air quality factors and Particular Matters (PMs) can help take precautionary actions and mitigate the disease risks. PMs are conventionally measured by stationary monitors, which are not homogeneously distributed. Therefore, this research aims to propose a personal portable PM measuring device installed in multiple urban locations to record real-time data on PM concentration and monitor the air quality measures efficiently. The area near the department of Architecture, Built environment and Construction Engineering of Politecnico di Milano was selected as a case study

Prioritization and Evaluation of Mechanical Components Failure of CNC Lathe Machine based on Fuzzy FMEA Approach

Introduction In recent years, with development of industrial products with complex and precise systems, the demand for CNC machines has been increasing, and as its technology has been progressed, more failure modes have been developed with complex and multi-purpose structures. The necessity of CNC machines’ reliability is also more evident than ever due to its impact on production and its implementation costs. Aiming at reducing the risks and managing the performance of the CNC machine parts in order to increase the reliability and reduce the stop time, it is important to identify all of the failure modes and prioritize them to determine the critical modes and take the proper cautionary maintenance actions approach. Materials and Methods      In this study, conventional and fuzzy FMEA, which is a method in the field of reliability applications, was used to determine the risks in mechanical components of CNC lathe machine and all its potential failure modes. The extracted information was mainly obtained by asking from CNC machine experts and analysts, who provided detailed information about the CNC machining process. These experts used linguistic terms to prioritize the S, O and D parameters. In the conventional method, the RPN numbers were calculated and prioritized for different subsystems. Then in the fuzzy method, first the working process of the CNC machine and the mechanism of its components were studied. Also, in this step, all failure modes of mechanical components of the CNC and their effects were determined. Subsequently, each of the three parameters S, O, and D were evaluated for each of the failure modes and their rankings. For ranking using the crisp data, usually, the numbers in 1-10 scale are used, then using linguistic variables, the crisp values are converted into fuzzy values (fuzzification). 125 rules were used to control the output values for correcting the input parameters (Inference). For converting input parameters to fuzzy values and transferring qualitative rules into quantitative results, Fuzzy Mamdani Inference Algorithm was used (Inference). In the following, the inference output values are converted into non-fuzzy values (defuzzification). In the end, the fuzzy RPNs calculated by the fuzzy algorithm and defuzzified are ranked. Results and Discussion In conventional FMEA method, after calculating the RPNs and prioritizing them, the results showed that this method grouped 30 subsystems into 30 risk groups due to the RPN equalization of some subsystems, while it is evident that by changing the subsystem, the nature of its failure and its severity would vary. Therefore, this result is not consistent with reality. According to the weaknesses of this method, fuzzy logic was used for better prioritization. In the fuzzy method, the results showed that, in the 5-point scale, with the Gaussian membership function and the Centroid defuzzification method, it was able to prioritize subsystems in 30 risk groups. In this method, gearboxes, linear guideway, and fittings had the highest priority in terms of the criticality of failure, respectively. Conclusions The results of the fuzzy FMEA method showed that, among the mechanical systems of CNC lathe machine, the axes components and the lubrication system have the highest FRPNs and degree of criticality, respectively. Using the fuzzy FMEA method, the experts' problems in prioritizing critical modes were solved. In fact, using the linguistic variables enabled experts to have a more realistic judgment of CNC machine components, and thus, compared to the conventional method, the results of the prioritization of failure modes are more accurate, realistic and sensible. Also, using this method, the limitations of the conventional method were reduced, and failure modes were prioritized more effectively and efficiently. Fuzzy FMEA is found to be an effective tool for prioritizing critical failure modes of mechanical components in CNC lathe machines. The results can also be used in arranging maintenance schedule to take corrective measures, and thereby, it can increase the reliability of the machining process

Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide-area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi-objective problem (MOP) is proposed as a mixed-integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced

A new robust multi-machine power system stabilizer design using quantitative feedback theory

Small-signal oscillations is one of the important problems in power system operation that caused by insufficient natural damping in the system. This paper uses the Quantitative Feedback Theory (QFT) to design a new robust PSS for multi-machine power systems able to provide acceptable damping over a wide range of operating points. In the design procedure the main purpose is to reject the load fluctuations and, therefore, a particular transfer function is used as the nominal plant. The parametric uncertainty in power system is readily handled using QFT. The decentralized design with a simple structure is easily applied to multi-machine power systems. The nonlinear time-domain simulations are carried out to validate the effectiveness of the proposed controller. Results clearly show the benefits of the proposed controller for stability enhancement of power systems