Civil Engineering June 2022 | Vol 30 No 5

Civil Engineering June 2022 47 From a technical standpoint, a smart city is a city that uses technology to provide services and solve city problems. The main goal of a smart city is to optimise city functions and promote economic growth while also improving the quality of life for citizens by using smart technologies and data analysis. By digitising and creating a living virtual representation of major infrastruc- ture and contributing factors in a city, IoT, mass data and data analytics provide enhanced insights and decision making to ensure the best or optimal options are taken/implemented relative to citizens, resources, environment, etc. When it comes to measuring the true smartness/effectiveness of DTs, several indicators are used. The number of indicators/criterions used to gauge the true smartness of a smart city varies across the literature, but six key smart city indicators emerge: Q Q Smart government Q Q Smart economy Q Q Smart environment Q Q Smart living Q Q Smart mobility Q Q Smart people With a data-rich BIM model and a well thought out IoT ecosystem, decisions can be made on a city level, enhancing the quality of life for citizens based on mass data analytics and analysis. CONCEPTS IN PRACTICE With a National State of Disaster declared in response to the floods that devastated KwaZulu-Natal in April 2022, I thought it prudent to consider whether a DT could have prevented the damage caused. The short answer is no, as the flooding that took place was an act of God. However, smart technologies could have better equipped us to tackle instances of this nature and curb/reduce the damages/‌impacts. One of the major benefits of a smart city is predictive analysis. With data constantly looping and feeding into the model, predictions could have been computed based on past historical data and occurrences from a climatic and geotechnical perspective, as well as incorporation of terrain and GIS data highlighting key geospatial elements. With this computational advantage, mass data could propose solutions or key points of danger/risk, allowing us to prepare for the worst-case scenario. These flagged areas and hazard points could then be reanalysed by the appro- priate professionals and a course of action could be taken, with the advantage of running simulations of various scenarios for a much wider lens of problem solving and risk management. Slope stability monitoring systems such as those used in mining sites could be established at key danger or high- risk areas to monitor and predict any landslides or failure in slope stability. Technologies could also be used to monitor levels in dams, rivers, and other water bodies to ensure levels are within optimum ranges, serving as a means to conserve water, dispose of water and protect ourselves from flooding in an effective manner. Flood simulations on a variety of return periods could also be conducted to gauge the reach of water and plan as best as possible. Switching from a preventative to a reactive approach, these same methodolo- gies can be adapted in times of disaster to, for example, analyse affected infrastruc- ture such as roads, and classify or rate them based on the level of risk. This could include proposing or highlighting route alternatives in an event where main roads and highways are rendered unsafe and unusable. This would enable effective use of available func- tioning road networks, ensuring the safety of commuters and citizens, as well as serving as a means to dispatch and direct emergency services to their destination in the fastest and safest way possible. The distribution of food, water, and other necessities to effected communities could also be effectively planned in the most viable manner. Data and findings from the disaster event could then be fed into the DT post event to compute future scenarios and predictions, and with enough quality data, could be used to flag an upcoming act of nature, rate its magnitude, and indicate whether the impacts would render an area a disaster zone. As we can see, the power, possibilities and advan- tages of smart cities are huge, and show great promise. In summary, a DT equals or equates to smart infrastructure assets and/or smart cities, which BIM alone cannot. BIM does not equal a DT, but is rather the most effective and efficient path to achieving a DT, with the BIM model serving as the foundation. With smart solutions, engineering and high-mass data qualita- tive processing and graphical analytics, smart cities and/or DTs are the future of resilient, intelligent, and futuristic infrastructure.