For prediction, we mean to foretell the occurring of an event in advance according to hypotheses based on mathematical calculi.
Within the word “prediction”, are stored all the essence and the desire that drive monitoring. Today, we can’t be happy with the mere knowledge of what’s happening to a thing in real-time. We need to predict the future on the basis of past events.
Monitoring means the continuous observation of a thing, physical size, or of the descriptive mathematics of a thing. The thing constitutes the asset (tangible or intangible) that must be monitored constantly in time.
Time is the fundamental measure the governs monitoring. It’s obvious that in our reality the “spread of time” is continuous and can be discerned. In other words, we can claim that in the real world the concept of atomicity is in force, meaning that if we take a time interval, we can then segment it into infinite instants. In monitoring, instead, we cannot “continuously” grasp (or acquire) a physical measure. In mathematical terms, there is no such frequency of infinite data acquisition. That’s why we have to settle for moderate data acquisitions scanned at a certain frequency.
The frequency of acquisition is responsible for the subdivision of monitoring in two macro areas:
“High” and “low” frequencies are quantified according to their application. For instance, in infrastructure monitoring, the boundary between dynamic and static can be drawn around 10-20 Hz.
All the measures subject to monitoring are characterized by a progression in time whose variations can depend on other variants which, in turn, may or may not be subject to monitoring. According to this, monitoring makes up for an incredible tool to identify correlations among physical measures in a monitoring system.
In any case, however we may employ it, whoever works with monitoring tools must know how to use a set of Time Series that characterize any acquired physical measure.
In ogni caso, a prescindere dall’uso che se ne fa, chiunque opera nel monitoraggio deve destreggiarsi con una serie di Time Series caratteristiche di ogni grandezza fisica acquisita.
Prediction, in monitoring, makes up the meeting point between civil-environmental engineering and data science.
All the civil works built in the modern-contemporary age are designed according to the laws of the Science of Construction and Statics which dictate the rules on the dimensions of beams and load-bearing structural elements. Moreover, they take into account how the work interacts with itself and the surrounding environment.
thus, civil-environmental engineering provides us the main engineering properties describing the nominal conditions of a project - that is the final, expected numerical values. In other words, the nominal value of engineering properties constitutes the values at the work’s beginning and then changes during the normal life of the work.
The observation of variation in time of these engineering values is the goal of structural monitoring.
Actually, in the design phase, we already carry out probabilistic predictions. Many parameters and coefficients coming into play at this stage are calculated on a statistical basis and according to historical data of other structures or to lab tests.
Moreover, predictions inform us that the future behavior of the work is calculated on past, characteristic data of the same work. Indeed, however similar the behavior of two structures designed in the same fashion can be, it can never be identical. The power of prediction in monitoring dwells precisely in knowing the history of the piece of infrastructure, how it “breathes” in the succession of day and night and how it reacts to external stimuli. Once we know the history, then we can determine the trends that inform us about the properties we’re examining, we can set thresholds for the predictive maintenance, above which we must intervene.
To set thresholds we must have a thorough and reliable knowledge of the piece of infrastructure's history. thresholds can be static, adaptive, and dynamic. They are calculated with historical data and selected on the basis of the type of monitoring to carry out. Anyway, predictive maintenance takes shape from the continuous comparison between different kinds of thresholds with the considered engineering properties.
The meeting point between civil-environmental engineering and data science calls for Machine Learning too. This is a helpful tool when it comes to monitoring and prediction since we can identify instances of the system based on classification and clustering by means of correlation algorithms.
A typical example is the application of classification algorithms on the correlation between two or more characteristics of the system, from which stem sub-dominions of existence. The constant monitoring of correlation provides us with further information on the past and current behavior of the structure. Thus, we can determine future trends and potential anomalies.
In the future, we will see an increasing need for monitoring systems. The fields of application are the widest and involve the use of several technologies and researches. We go from sensors (electric, optic fiber) to civil engineering, going through data science, computer science, and statistics. This dictates a multidisciplinary approach involving different profiles that have to communicate effectively with each other.
Today, we still don’t have a widespread culture of multidisciplinary monitoring - meaning that it is still a too-much underestimated subject to justify investments in the necessary resources. With these conditions, monitoring is left to not enough automated tools causing a lack of data and, consequently, a scarce knowledge on the life of the work during its normal operations. This doesn’t help the subject of monitoring to thrive.
Larger interest only comes from catastrophic events such as bridges and flyovers falling down which pushed the investments in new technologies to monitor pieces of infrastructure 24h and in real time. Moreover, the majority of roads and railways were built in the ‘50s and in the '60s. Hence, we’re almost at the end of the useful life of these works and are already showing signs of failure. These, indeed, are encouraging the installation of monitoring systems, although they are not supported by the necessary consideration they deserve.
The subject’s multidisciplinarity needs us to keep up with the times. Innovative technologies can become obsolete in few years. For instance, in a few years, Machine Learning has become a widely renowned subject and might even become obsolete in a while. That’s why we need to start thinking about applications of Deep Learning or non-supervised algorithms.
Tell me your opinion on the topic or what should I write next! If you missed it, also read my first post in the Tech Coffee Break column, dedicated to edge technologies!
THE AUTHOR: ANDREA CANFORA
Senior Data Scientist at Sensoworks. His cross-field engineering skills allow him to be the joining link between the teams that design Sensoworks’ solutions and the technical team that realizes them.
When we talk of infrastructure monitoring, the main method this is carried out with is the active monitoring of the piece of infrastructure to control. We’ll see the fundamental elements and the benefits of constant monitoring on a given piece of infrastructure later, what technologies are disrupting the market and what it really means for you to implement a monitoring system in your daily management.
The recent global recession did not stop the growing trends of the infrastructure market (Deloitte 2020 Midyear Update), where developing states are increasingly challenging the status quo of an otherwise Western-controlled market, China being the foremost emergent player.
The One Belt One Road initiative, for one, is strong of a US$667 trillion infrastructure funding package China put forward to strengthen the status of infrastructure in and out the country, while the US also passed a bill to allocate US$1.5 trillion for their infrastructure.
If we put into perspective the fact that China, the US, and Europe make up over 42% of the infrastructure investment gap in 2020, 2030 and 2040 - with the US having the highest gap - we can also see the potential for the market to keep receiving the attention of governments and multinational companies alike to take part in a growing trend in the following years (Franklin Templeton).
However, although this gives us a brief overview of the global infrastructure’s situation, we should not forget that infrastructure is not only monumental bridges and skyscrapers. Infrastructure is also residential buildings, roads, smaller bridges, highways, and so on. Infrastructure is all the systems of connections, utilities, services, that people need on a daily basis. We all need our neighborhoods renovated, our rivers tamed, our waste managed.
The state of the art of today’s infrastructure gives us reason to be optimistic on the many future opportunities on the global market, but these will need to be under constant attention in terms of investments and continuous maintenance. Infrastructure cannot be ignored under any circumstances, especially if it deals with critical situations, such as the people’s daily lives and regional economic well being.
Maintenance, in particular, can be better managed using some of the newest technologies available.
Sensor technology, for instance, can help us collect the necessary data to constantly check the health status of a given piece of infrastructure in real-time (partially or entirely) during its entire life and operations.
Basically, sensors are the “spies” strategically placed on the infrastructure that tell us every single time what’s going on and what might happen if we don’t hastily intervene. The way they do this is through the collection of small data immediately sent on the cloud through their connectivity hardware. We can divide these sensors in:
After data is collected by sensors, it must be processed, translated and made easily understandable for the human mind. This is where IoT platforms come into play.
In brief, an IoT platform is a multilayer technology which “digitizes” (that is, brings into the online world) the physical objects, the “things” of the IoT, allowing for machine to machine communication with no need for human interjection.
There are several types of IoT platforms, although platforms are evolving to integrate all the different aspects of IoT into one solution, such as end-to-end platforms, cloud platforms or on-premise installations, AIoT (literally, the Artificial Intelligence of things), and so on.
However, besides all their different characteristics and capabilities, all IoT platforms have something in common: they all act as middleware or as data plumbers to connect devices and applications to an end. This by means of an ecosystem of sensors, controllers, gateway devices, data analyzing and translating software, end application services, and much more.
If we should try and sketch an elementary IoT architecture, we could divide it into four stages:
In the end, what humans are provided with, is the translation of fundamental data in the form of graphically enjoyable dashboards, understandable parameters, alerts and notifications to remind us what’s due and when, and much more. All these elements support human intervention when and where needed, almost as if they give us the power to read into the future!
This power has a name: predictive maintenance. Its meaning is in its name: to predict the moment a building, a bridge, a highway or whatnot is in need of maintenance.
Predictive maintenance is possibly one of the most important benefits of a good IoT monitoring system. With it, we can understand not only the moment a piece of infrastructure needs maintenance but also the exact spot, allowing for more specific and ad-hoc predictive interventions, rather than periodic inspections of the entire infrastructure.
And well, less periodic, mass inspections and more specific, aimed interventions mean one crucial thing: decreased maintenance costs weighing on the annual budget. Predicting potential harm or danger allows us to save money we otherwise would spend on replacement of material, in personnel deployment, in the actual size of the intervention, since the damage is stopped before it could grow into something bigger.
Not only, though: more precise interventions, together with a constant eye on the infrastructure's health status, can help in preventing destruction and damage due to decay or degradation or to keep track of the precise effects of the consequences of extraordinary events (such as earthquakes, floods, etc.). This way, increasing safety and strengthening the resilience of each monitored infrastructure.
Infrastructure is the basis of our lives, our connections, our economics. They connect distant families and allow us to travel, to live the city life, to improve our impact on the Earth.
A sounder monitoring system for our infrastructure would allow us to enjoy and to exploit more efficient and safer roads, bridges, dams, sewage grids, buildings, besides the possibility to reinvest the money saved in fewer periodic interventions.
The benefits of adopting this kind of solution encompass all the value chain from the construction to the management of every piece of infrastructure, whatever it is and for whatever purpose.
That is Sensoworks’s main goal. To move towards an all-round improvement and renovation of the infrastructure industry and market, to overhaul the very concept we have of infrastructure today.
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