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Construction Materials


LafargeHolcim launches Industry 4.0 for cement production – Australian Bulk Handling Review

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LafargeHolcim will implement automation and robotics, artificial intelligence, predictive maintenance and digital twin technologies for its production process. The company is upgrading its production fleet for the future through its'Plants of Tomorrow" program. The program will be rolled out over four years as LafargeHolcim upgrades its technologies in the building materials industry. The company predicts a "Plants of Tomorrow" certified operation will show 15 to 20 percent of operational efficiency gains compared to a conventional cement plant. Among the technologies implemented are predictive operations that can detect abnormal conditions and process anomalies in real-time. This aims to reduce maintenance costs by more than 10 percent and significantly lower energy costs. Digital twins of plants will also be created to optimise training opportunities. Automation and robotics is another important element of the strategy. Unmanned surveillance is being performed for high exposure jobs in the entire plant. Partnering with Swiss start-up Flyability, the company is using drones that allow the frequency of inspections to increase while simultaneously reducing cost and increasing safety for employees by inspecting confined spaces. In addition, the new PACT (Performance and Collaboration) digital tool allows operational decision making from experience-based to data-centric, by combining data from various sources and enabling machine learning applications. LafargeHolcim is currently working on more than 30 pilot projects covering all regions where the company is active. The first integrated cement plant will be at LafargeHolcim's premises in Siggenthal, Switzerland, this plant will test all modules of the'Plants of Tomorrow' program. LafargeHolcim Global Head Cement Manufacturing, Solomon Baumgartner Aviles, said transforming the way we produce cement is one of the focus areas of our digitalisation strategy and the'Plants of Tomorrow' initiative will turn Industry 4.0 into reality at our plants. "These innovative solutions make cement production safer, more efficient and environmentally fit.


AI planners in Minecraft could help machines design better cities

MIT Technology Review

The open-endedness of the challenge means that AIs need to master multiple objectives. To win, they must impress eight human judges from a range of backgrounds, including architects, archaeologists, and game designers. These judges score the AI city planners in four areas: how well they adapt their designs to specific locations; how well the layouts work, according to criteria such as whether there are bridges and roads between different areas; how appealing they are aesthetically; and how much the designs evoke a narrative--are there details that tell a story about how a town came to be, such as a ruin or a pit from which building materials might have been mined? "Making a Minecraft village for an unseen map is something a 10-year-old human could do," says Salge. "But it is really difficult for an AI." For example, one entrant started by identifying the type of environment--desert or forest, say--and then generated buildings that looked as if they had been built out of common local materials.


HS2 tests new AI technology to trim carbon emissions

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The UK's HS2 has trialled a new artificial intelligence-based carbon and cost estimating solution to decrease carbon emissions. The technology was trialled at several HS2 locations managed by the Skanska Costain STRABAG joint venture. The solution helps in automating building information model (BIM) processes. It enables simulating multiple design options using different combinations and types of construction materials. The process helps in measuring and comparing the environmental impacts and carbon emissions for each simulation and accordingly design a cost-effective and environmentally friendly construction model.


Machine Learning saves Energy by predicting accuracy of weather forecasts – RtoZ.Org – Latest Technology News

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Sophisticated heating and cooling systems in Buildings adjust themselves based on the predicted weather. But when the forecast is imperfect – as it often is – buildings can end up wasting energy. A new approach developed by Cornell Researchers predicts the accuracy of the weather forecast using a machine learning model trained with years' worth of data on forecasts and actual weather conditions. The Researchers combined that predictor with a mathematical model that considers building characteristics including the size and shape of rooms, the construction materials, the location of sensors and the position of windows. The result is a smart control system that can reduce energy usage by up to 10 percent, according to a case study the research team conducted on Toboggan Lodge, a nearly 90-year-old building on Cornell's campus.


Optimal Inspection and Maintenance Planning for Deteriorating Structures through Dynamic Bayesian Networks and Markov Decision Processes

arXiv.org Artificial Intelligence

Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.


Deep learning for mechanical property evaluation

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A standard method for testing some of the mechanical properties of materials is to poke them with a sharp point. This "indentation technique" can provide detailed measurements of how the material responds to the point's force, as a function of its penetration depth. With advances in nanotechnology during the past two decades, the indentation force can be measured to a resolution on the order of one-billionth of a Newton (a measure of the force approximately equivalent to the force you feel when you hold a medium-sized apple in your hand), and the sharp tip's penetration depth can be captured to a resolution as small as a nanometer, or about 1/100,000 the diameter of a human hair. Such instrumented nanoindentation tools have provided new opportunities for probing physical properties in a wide variety of materials, including metals and alloys, plastics, ceramics, and semiconductors. But while indentation techniques, including nanoindentation, work well for measuring some properties, they exhibit large errors when probing plastic properties of materials -- the kind of permanent deformation that happens, for example, if you press your thumb into a piece of silly putty and leave a dent, or when you permanently bend a paper clip using your fingers.


The Convergence of AI and Structural Engineering

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Technology is supposed to have a positive effect on humanity. That was the initial vision, correct? But for some reason this artificial intelligence hype has become a controversy and the new space race all in one. On one hand, Elon Musk, CEO of Tesla, says he's taking a cautious approach to the emerging technology. Musk says it's the most serious threat to the survival of the human race [1].


This 3D printed house reduces carbon emissions and takes 48 hours to build!

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The construction industry contributes to 39% of global carbon emissions while aviation contributes to only 2% which means we need to look for alternative building materials if we are to make a big impact on the climate crisis soon. We've seen buildings being made using mushrooms, bricks made from recycled plastic and sand waste, organic concrete, and now are seeing another innovative solution – a floating 3D printed house! Prvok is the name of this project and it will be the first 3D printed house in the Czech Republic built by Michal Trpak, a sculptor, and Stavebni Sporitelna Ceske Sporitelny who is a notable member of the Erste building society. The house is designed to float and only takes 48 hours to build! Not only is that seven times faster than traditional houses, but it also reduces construction costs by 50%.


Machine-learning-based methods for output only structural modal identification

arXiv.org Machine Learning

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex cost function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach s good capability in blindly extracting modal information from system responses.


A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications

arXiv.org Machine Learning

Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.