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Authors: Mohsen Abdelrazek Khorshid Ali Selim

Abstract:

Disadvantages and errors of current concrete reinforcement methodsL: Current concrete reinforcement methods are adopted in most parts of the world in their various doctrines and names. They adopt the so-called concrete slab system, where these slabs are semi-independent and isolated from each other and from the surrounding environment of concrete columns or beams, so that the reinforcing steel does not cross from one slab to another or from one slab to adjacent columns. It or the beams surrounding it and vice versa are only a few centimeters and no more. The same applies exactly to the concrete columns that support the building, where the reinforcing steel does not extend from the slabs or beams to the inside of the columns or vice versa except for a few centimeters and no more, just as the reinforcing steel does not extend from inside the column at the top. The ceiling is only a few centimetres, and the same thing is literally repeated in the concrete beams that connect the columns and separate the slabs, where the reinforcing steel does not cross from one beam to another or from one beam to the slabs or columns adjacent to it and vice versa, except for a few centimeters, which makes the basic building elements of columns, slabs and beams They all work in isolation from each other and from the environment surrounding them from all sides. This traditional method of reinforcement may be valid and lasting in geographical areas that are not exposed to earthquakes and earthquakes, where all the loads and tensile forces in the building are constantly directed vertically downward due to gravity and are borne directly by the vertical reinforcement of the building. However, in the case of earthquakes and earthquakes, the loads and tensile forces in the building shift from the vertical direction to the horizontal direction at an angle of inclination that depends on the strength of the earthquake, and most of them are borne by the horizontal reinforcement extending between the basic elements of the building, such as columns, slabs and beams, and since the crossing of the reinforcement between each of the columns, slabs and beams between them And each other, and vice versa, does not exceed several centimeters. In any case, the tensile strength, cohesion and bonding between the various parts of the building are very weak, which causes the buildings to disintegrate and collapse in the horrific manner that we saw in the earthquake in Turkey and Syria in February 2023, which caused the collapse of tens of thousands of buildings in A few seconds later, it left more than 50,000 dead, hundreds of thousands injured, and millions displaced. Description of the new earthquake-resistant model: The idea of the new model in the reinforcement of concrete buildings and constructions is based on the theory that we have formulated as follows: [The tensile strength, cohesion and bonding between the basic parts of the concrete building (columns, beams and slabs) increases as the lengths of the reinforcing steel bars increase and they extend and branch and the different parts of the building share them with each other.] . In other words, the strength, solidity, and cohesion of concrete buildings increase and they become resistant to earthquakes as the lengths of the reinforcing steel bars increase, extend, branch, and share with the various parts of the building, such as columns, beams, and slabs. That is, the reinforcing skewers of the columns must extend in their lengths without cutting to cross from one floor to another until their end. Likewise, the reinforcing skewers of the beams must extend in their lengths without cutting to cross from one beam to another. The ends of these skewers must rest at the bottom of the columns adjacent to the beams. The same thing applies to the reinforcing skewers of the slabs where they must These skewers should be extended in their lengths without cutting to cross from one tile to another, and the ends of these skewers should rest either under the adjacent columns or inside the beams adjacent to the slabs as follows: First, reinforce the columns: The columns have the lion's share of the reinforcing steel in this model in terms of type and quantity, as the columns contain two types of reinforcing bars. The first type is large-diameter bars that emerge from the base of the building, which are the nerves of the column. These bars must extend over their normal length of 12 meters or more and extend to a height of three floors, if desired. In raising other floors, bars with the same diameter and the same length are added to the top after the second floor. The second type is bars with a smaller diameter, and they are the same ones that are used to reinforce beams and slabs, so that the bars that reinforce the beams and slabs facing each column are bent down inside this column and along the entire length of the column. This requires an order. Most engineers do not prefer it, which is to pour the entire columns and pour the roof at once, but we prefer this method because it enables us to extend the reinforcing bars of both the beams and slabs to the bottom of the columns so that the entire building becomes one concrete block that is cohesive and resistant to earthquakes. Secondly, arming the cameras: The beams' reinforcing skewers must also extend to a full length of 12 meters or more without cutting. The ends of the skewers are bent and dropped inside the column at the beginning of the beam to its bottom. Then the skewers are extended inside the beam so that their other end falls under the facing column at the end of the beam. The skewers may cross over the head of a column. Another passes through another adjacent beam and rests at the bottom of a third column, according to the lengths of each of the skewers and beams. Third, reinforcement of slabs: The slab reinforcing skewers must also extend their entire length, 12 meters or more, without cutting, distinguishing between two cases. The first case is the skewers opposite the columns, and their ends are dropped inside one of the columns. Then the skewers cross inside the adjacent slab and their other end falls below the opposite column. The skewers may cross over The head of the adjacent column passes through another adjacent slab and rests at the bottom of a third column, according to the dimensions of the slabs and the lengths of the skewers. The second case is the skewers opposite the beams, and their ends must be bent in the form of a square or rectangle according to the dimensions of the beam’s width and height, and this square or rectangle is dropped inside the beam at the beginning of the slab, and it serves as The skewers are for the beams, then the skewers are extended along the length of the slab, and at the end of the slab, the skewers are bent down to the bottom of the adjacent beam in the shape of the letter U, after which the skewers are extended inside the adjacent slab, and this is repeated in the same way inside the other adjacent beams until the end of the skewer, then it is bent downward in the form of a square or rectangle inside the beam, as happened. In its beginning.

Keywords: earthquake resistant buildings, earthquake resistant concrete constructions, new technology for reinforcement of concrete buildings, new technology in concrete reinforcement

Procedia PDF Downloads 47

Authors: Jessica Ho

Abstract:

AI has helped government, businesses and industries transform the way they do things. AI is used in automating tasks to improve decision-making and efficiency. AI is embedded in sensors and used in automation to help save time and eliminate human errors in repetitive tasks. Today, we saw the growth in AI using the collection of vast amounts of data to forecast with greater accuracy, inform decision-making, adapt to changing market conditions and offer more personalised service based on consumer habits and preferences. Government around the world share the opportunity to leverage these disruptive technologies to improve productivity while reducing costs. In addition, these intelligent solutions can also help streamline government processes to deliver more seamless and intuitive user experiences for employees and citizens. This is a critical challenge for NSW Government as we are unable to determine the risk that is brought by the unprecedented pace of adoption of AI solutions in government. Government agencies must ensure that their use of AI complies with relevant laws and regulatory requirements, including those related to data privacy and security. Furthermore, there will always be ethical concerns surrounding the use of AI, such as the potential for bias, intellectual property rights and its impact on job security. Within NSW’s public sector, agencies are already testing AI for crowd control, infrastructure management, fraud compliance, public safety, transport, and police surveillance. Citizens are also attracted to the ease of use and accessibility of AI solutions without requiring specialised technical skills. This increased accessibility also comes with balancing a higher risk and exposure to the health and safety of citizens. On the other side, public agencies struggle with keeping up with this pace while minimising risks, but the low entry cost and open-source nature of generative AI led to a rapid increase in the development of AI powered apps organically – “There is an AI for That” in Government. Other challenges include the fact that there appeared to be no legislative provisions that expressly authorise the NSW Government to use an AI to make decision. On the global stage, there were too many actors in the regulatory space, and a sovereign response is needed to minimise multiplicity and regulatory burden. Therefore, traditional corporate risk and governance framework and regulation and legislation frameworks will need to be evaluated for AI unique challenges due to their rapidly evolving nature, ethical considerations, and heightened regulatory scrutiny impacting the safety of consumers and increased risks for Government. Creating an effective, efficient NSW Government’s governance regime, adapted to the range of different approaches to the applications of AI, is not a mere matter of overcoming technical challenges. Technologies have a wide range of social effects on our surroundings and behaviours. There is compelling evidence to show that Australia's sustained social and economic advancement depends on AI's ability to spur economic growth, boost productivity, and address a wide range of societal and political issues. AI may also inflict significant damage. If such harm is not addressed, the public's confidence in this kind of innovation will be weakened. This paper suggests several AI regulatory approaches for consideration that is forward-looking and agile while simultaneously fostering innovation and human rights. The anticipated outcome is to ensure that NSW Government matches the rising levels of innovation in AI technologies with the appropriate and balanced innovation in AI governance.

Keywords: artificial inteligence, machine learning, rules, governance, government

Procedia PDF Downloads 51

Authors: Marco F. Benaglia, Mei H. Chen, Kune M. Tsai, Chia H. Hung

Abstract:

As societal and family structures, consumer dietary habits, and awareness about food safety and quality continue to evolve in most developed countries, the demand for refrigerated and frozen foods has been growing, and the issues related to their preservation have gained increasing attention. A well-established cold chain logistics system is essential to avoid any temperature abuse; therefore, assessing potential disruptions in the operational processes of cold chain logistics centers becomes pivotal. This study preliminarily employs HACCP to find disruption factors in cold chain logistics centers that may cause temperature abuse. Then, concept mapping is applied: selected experts engage in brainstorming sessions to identify any further factors. The panel consists of ten experts, including four from logistics and home delivery, two from retail distribution, one from the food industry, two from low-temperature logistics centers, and one from the freight industry. Disruptions include equipment-related aspects, human factors, management aspects, and process-related considerations. The areas of observation encompass freezer rooms, refrigerated storage areas, loading docks, sorting areas, and vehicle parking zones. The experts also categorize the disruption factors based on perceived similarities and build a similarity matrix. Each factor is evaluated for its impact, frequency, and investment importance. Next, multiple scale analysis, cluster analysis, and other methods are used to analyze these factors. Simultaneously, key disruption factors are identified based on their impact and frequency, and, subsequently, the factors that companies prioritize and are willing to invest in are determined by assessing investors’ risk aversion behavior. Finally, Cumulative Prospect Theory (CPT) is applied to verify the risk patterns. 66 disruption factors are found and categorized into six clusters: (1) "Inappropriate Use and Maintenance of Hardware and Software Facilities", (2) "Inadequate Management and Operational Negligence", (3) "Product Characteristics Affecting Quality and Inappropriate Packaging", (4) "Poor Control of Operation Timing and Missing Distribution Processing", (5) "Inadequate Planning for Peak Periods and Poor Process Planning", and (6) "Insufficient Cold Chain Awareness and Inadequate Training of Personnel". This study also identifies five critical factors in the operational processes of cold chain logistics centers: "Lack of Personnel’s Awareness Regarding Cold Chain Quality", "Personnel Not Following Standard Operating Procedures", "Personnel’s Operational Negligence", "Management’s Inadequacy", and "Lack of Personnel’s Knowledge About Cold Chain". The findings show that cold chain operators prioritize prevention and improvement efforts in the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster, particularly focusing on the factors of "Temperature Setting Errors" and "Management’s Inadequacy". However, through the application of CPT theory, this study reveals that companies are not usually willing to invest in the improvement of factors related to the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster due to its low occurrence likelihood, but they acknowledge the severity of the consequences if it does occur. Hence, the main implication is that the key disruption factors in cold chain logistics centers’ processes are associated with personnel issues; therefore, comprehensive training, periodic audits, and the establishment of reasonable incentives and penalties for both new employees and managers may significantly reduce disruption issues.

Keywords: concept mapping, cold chain, HACCP, cumulative prospect theory

Procedia PDF Downloads 46

Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

Abstract:

Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

Procedia PDF Downloads 77

Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

Abstract:

As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

Procedia PDF Downloads 98