Service Load Research Articles

LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.

Flexural behavior and serviceability performance of SSC beams reinforced with hybrid GFRP-stainless steel bars

Using fiber-reinforced polymer (FRP) bars instead of steel bars to reinforce concrete structures has become an important method to solve the corrosion problems. FRP bars reinforced concrete (FRP-RC) beams suffer sudden failure owing to the brittleness of FRP bars. Therefore, it is suggested to add steel reinforcements in FRP-RC beams to improve the ductility. This study analyzes the flexural performance of seawater and sea sand concrete (SSC) beams reinforced with hybrid GFRP and stainless steel (SS) bars, in which GFRP bars are used as tension bars while SS bars are used as compression bars. The flexural performances of GFRP bars reinforced SSC beams and SS bars reinforced SSC beams were also studied for comparison. Based on the test results, the cracking moment, load-carrying capacity, deflection, and ductility of the SSC beams are discussed and compared with the guidance in current design codes. Results show that ACI 440.1R-15 and CSA S806-12 reasonably predict the cracking moment and bearing capacity, but both underestimate the deflection at the service load. Furthermore, the moment capacity calculation equation is proposed with good accuracy. The ratio between the reinforcement ratios of GFRP bars and SS bars is suggested to meet the ductility requirement.

Serviceability behavior of High Strength Concrete I-beams reinforced with Carbon Fiber Reinforced Polymer bars

Fiber Reinforced Polymer (FRP) bars are anisotropic in nature and have high tensile strength in the fiber direction. The use of High-Strength Concrete (HSC) allows for better use of the high-strength properties of FRP bars. The mechanical properties of FRP bars can yield to large crack widths and deflections. As a result, the design of concrete elements reinforced with FRP materials is often governed by the Serviceability Limit States (SLS). This study investigates the short-term serviceability behavior of FRP RC I-beams. Eight RC I-beams reinforced with carbon-FRP (CFRP) and four steel RC I-beams, for comparison purposes, were tested under two-point loading. Deformations on the concrete and crack widths and spacing are measured and analyzed. A discussion on the main aspects of the SLS of FRP RC is introduced. The service load that fulfills the serviceability requirements, at a cross-section level, ranges between 0.27 and 0.38 times the ultimate load for sections dimensioned to fail in concrete crushing. The determinant criterion is the deflection limitation

Shear behaviour of seawater sea-sand coral aggregate concrete beams reinforced with FRP strip stirrups

The preparation of concrete for remote islands or reefs using locally available seawater, sea sand, and coral aggregate can greatly shorten the construction period and mitigate the shortage of river sand and natural gravel resources. This study is the first to experimentally investigate the shear behaviour of seawater sea-sand coral aggregate concrete (SSCAC) beams reinforced with non-corrosive carbon fibre-reinforced polymer (CFRP) strip stirrups. The test parameters included concrete strength, concrete type, and stirrup configuration. The experimental results suggested that CFRP-reinforced SSCAC beams exhibited a brittle shear failure mode because of the sudden CFRP rupture at the bend portions. The ratio of the measured maximum strain to the ultimate tensile strain of the CFRP strip stirrups varied from 36.6% to 47.8%. The critical diagonal shear cracks of SSCAC beams directly penetrated through the coral aggregates and split the aggregates, whereas those in the natural aggregate concrete (NAC) beams bypassed the natural gravel. In this regard, the SSCAC beams exhibited approximately 10% lower shear strength than their NAC counterparts owing to the loss of the aggregate interlocking effect after the occurrence of smooth diagonal shear cracks. The higher the strength grade of SSCAC, the greater the shear strength and service load.

Flexural and deflection behaviour of synthetic fibre reinforced concrete beams reinforced with glass fibre reinforced polymers bars under sustained service load

Glass fibre reinforced polymers (GFRP) bars have attracted increasing interest as a promising alternative for conventional steel reinforcement for its contribution towards sustainability and significantly improving the flexural performance of structural members. To mitigate the excessive deflections and crack widths exhibited by the FRP reinforced concrete members, fibre reinforced concrete (FRC) is employed which improves the toughness and cracking capacity of members. However, available research on the structural performance of FRC has focused on metallic fibres. The research presented in this paper is aimed at experimentally evaluating the influence of synthetic fibres on the time-dependent flexural behaviour of FRC beams reinforced with GFRP bars. Eight simply supported beams with varying synthetic fibre contents and GFRP bar diameters (but same reinforcement ratio) were tested under sustained service load. Creep under compression, concrete shrinkage, cracking moment, and deflection behaviour over 90 days were monitored and analyzed. Investigation revealed that the inclusion of synthetic fibres reduced the concrete deformations under creep and shrinkage by up to 22% and 26% respectively, increased the cracking capacity by 27%, increased the effective moment of inertia by 75%, and also resulted in up to 43% lower deflections. Suitability of several codes to predict the deformations and deflections under short and long-term conditions was also evaluated through comparison of the prediction models to the experimental results. Model proposed by ACI 440.1R-15 was found to predict the instantaneous deflection in beams without fibres with reasonable accuracy, however in FRC-GFRP beams, all models tend to overestimate the deflection. Presented research highlights the need for further investigation on non-metallic fibres and their contribution towards improvement of residual and flexural performance of FRC members to improve the validity of existing analytical prediction models.

Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing

Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by offloading computing tasks from vehicle terminals to edge servers. However, offloading of tasks increase in quantity the transmission time and energy of the network. In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted. The shared offloading strategy can be adapted to the complex scenarios of the IoVs. Each vehicle can share the offloading conditions of the VEC servers, and then adaptively select three computing modes: local execution, task offloading, and shared offloading. In this article, the network state and offloading strategy space are the input of the deep reinforcement learning (DRL). Through the DRL, each task unit selects the offloading strategy with the optimal energy consumption at each time period in the dynamic IoVs transmission and computing environment. Compared with the existing proposals and DRL-based algorithms, it can effectively reduce the delay and energy consumption required for tasks offloading.

A PROPOSAL FOR SEISMIC VULNERABILITY ASSESSMENT OF PRE-ENGINEERED STEEL BUILDINGS IN BANGLADESH USING SIMPLIFIED JAPANESE INDEX METHOD

At present seismic evaluation of structures has become very crucial in Bangladesh as the country is in earthquake prone region. Lots of studies have been performed and many methods have been proposed for the rapid seismic evaluation of reinforced concrete (RC) buildings. Over the past two decades the country has been going through an important evolution which is rapid construction of pre-engineered steel buildings (PEB). Unfortunately, there has not been a single research work done or any method proposed for the seismic evaluation of these type of structures. In this work, simplified structural evaluation which is based on Japanese Index Method has been used for the seismic evaluation of 05 existing PEB steel structure. The structural seismic index of the building, IBS has been compared with the seismic judgement index IBSO which is a function of seismic zone co-efficient, structural importance coefficient and normalized acceleration response spectrum. The service load index IBD has also been compared with service load judgement indices, IBD01 and IBD02 which has been calculated from the material strength. For example, from the judgement of one building at ground floor, 0.5IBSO ≦ IBS＜IBSO and IBD02＜IBDhave been observed hence immediate detail evaluation recommended.

Resource Allocation for 5G Network Considering Privacy Protection in Edge Computing Environment

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the “Internet of Everything” and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers.

Experimental and Numerical Behavior of Encased Pultruded GFRP Beams under Elevated and Ambient Temperatures

In this research, experimental and numerical studies were carried out to investigate the performance of encased glass-fiber-reinforced polymer (GFRP) beams under fire. The test specimens were divided into two peer groups to be tested under the effect of ambient and elevated temperatures. The first group was statically tested to investigate the monotonic behavior of the specimens. The second group was exposed to fire loading first and then statically tested to explore the residual behavior of the burned specimens. Adding shear connectors and web stiffeners to the GFRP beam was the main parameter in this investigation. Moreover, service loads were applied to the tested beams during the fire. Utilizing shear connectors, web stiffeners, and both enhanced the load-carrying capacities of the encased beams by 100.6%, 97.3%, and 130.8%, respectively. Comparisons between the burned and unburned peer beams were presented with losses in the load-carrying capacity of the burned beams. These losses were the highest in the cases of shear connectors and web stiffeners due to the obtained severe damage, which led to more reductions in the residual behavior of the burned beams. Numerical analyses were performed using the general-purpose finite element (FE) ABAQUS package to conduct a parametric study. The investigated parameters included the effect of the exposure duration and the temperature level. The results of the FE analysis showed good agreement with the experimental results. Additional reductions in the residual capacities of the fire-damaged beams were observed due to exposure to longer fire durations. The improvements in the beam capacities due to using shear connectors and web stiffeners relative to the reference beams under the same exposure time decreased as the exposure duration increased. Furthermore, increasing the temperature to 700 °C, 800 °C, 900 °C, and 950 °C caused reductions in the residual capacities by about 25%, 45%, 70%, and 80%, respectively, for the encased beams in comparison to their peers at ambient temperature.

Desain dan Analisis Sistem CyberShare Menggunakan Four Node Interplanetary File System (IPFS)

Cybersecurity information sharing is a proactive and collaborative measure in enhancing organizational security by exchanging cybersecurity information using a centralized repository service. However, in practice, the use of centralized services poses a threat to distributed denial-of-service (DDoS) attacks which can result in system failure and cause single point of failure as well as man-in-the-middle (MITM) attacks which can result in modification of information and theft of exchanged information. This threat results in a lack of user confidence in the confidentiality, integrity, and availability of information. This study proposes the design of a secure cybersecurity information sharing (CyberShare) system using a private interplanetary file system (IPFS) network as a decentralized information storage. Unlike centralized storage which only has a single-node, CyberShare systems use four-node IPFS interconnected with swarm keys as authentication keys. This system allows users to store and share information from the sender to the recipient of information, avoiding dependence on a central server and reducing server load. The results of the analysis show that the proposed CyberShare system can guarantee the confidentiality, integrity, and availability of cyber security information. CyberShare systems can enhance the security of the information exchanged so that organizations can safely share and utilize cybersecurity information.

Optimization of framed structures subjected to blast loading using equivalent static loads method

In this study, the optimum design of a three-dimensional framed steel structure subjected to blast loading is considered. The main idea of this research is to develop a practical formulation for the design optimization problem and to study the effect of including blast loads in the design process. The optimization problem is formulated to minimize the total weight of the structure subjected to American Institution of Steel Construction (AISC) strength requirements and blast design displacement constraints. The design variables for beams and columns are the discrete values of the W-shapes selected from the AISC tables. A car carrying 250 lbs of trinitrotoluene with a 50 ft standoff distance from the front face is modeled as the source of the blast loading. Pressure–time histories are calculated on the front, sides, roof, and rear faces of the structure. Since the problem functions are not differentiable with respect to the design variables, the gradient-based optimization algorithms cannot be used to solve the problem. Therefore, metaheuristic algorithms are used to solve the optimization problem. Linear and nonlinear dynamic analyses are carried out in the optimization process. The problems are solved using metaheuristic optimization with the equivalent static loads method (MOESL). In MOESL, the dynamic load is transformed into equivalent static loads (ESLs) then the linear static analysis is carried out in the optimization process. The problems are 4-bay × 4-bay × 3-story frames under serviceability and blast loading. It is shown that a penalty on the optimum structural weight is substantial for designing structures to withstand blast loads.

Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.

A micro‐CT investigation of densification in pressboard due to compression

AbstractAs a non‐destructive inspection method, micro‐computed tomography has been employed for determining local properties of a cellulose‐based product, specifically pressboard. Furthermore, by utilizing the determined properties in a detailed numerical model, by means of a finite element analysis, we demonstrate a continuum anisotropic viscoelastic‐viscoplastic model. Through such a combination of non‐invasive experiments with accurate computations in mechanics, we attain a better understanding of materials and its structural integrity at a pre‐production stage increasing the success of the first prototype. In detail, this combination of micro‐computed tomography and finite element analysis improves accuracy in predicting materials response by taking into account the local material variations. Specifically, we have performed indentation tests and scanned the internal structure of the specimen for analysing the densification patterns within the material. Subsequently, we have used a developed material model for predicting the response of material to indentation. We have computed the indentation test itself by simulating the mechanical response of high‐density cellulose‐based materials. In the end, we have observed that pressboard, having initially a heterogeneous density distribution through the thickness, shows a shift in the densification to the more porous part after indentation. The densification maps of the simulated results are presented by comparing with the experimental results. A reasonable agreement is observed between the experimental and the simulated densifications patterns, which suggests that the proposed methodology can be used to predict densification also for other fibre‐based materials during manufacturing or in service loading.

Developing Hydrostatic Beehive Brickwork

Abstract In this paper, a new masonry construction is proposed based on honey beehive cell internal geometry as a unique structure and hydrostatic pressure principle. The considered experimental program involved suggestion, manufacturing, testing and analysis of masonry specimens of honey beehive units’ arrangement as well as corresponding specimens of custom arrangement, two classes of cementations bonding mortars are used. Plan strain concept and Saint Venant’s principle are adopted to model and assign proper boundary conditions of testing specimens. The significant improvement of masonry construction bearing capacity is confirmed by the obtained results and could be related to the presence of internal or self-confining pressure, which is produced due to the specific internal geometry of proposed honey beehive units’ arrangement of hexagonal construction units. The obtained results show that, the masonry specimens of proposed honey beehive arrangement Mode II exhibited higher bearing capacity in term of ultimate and service loads besides stiffness improvement in comparison with the customary arrangement Mode I.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Behavior of large-scale columns strengthened with basalt fiber-reinforced polymers sheets or bars and hybrid FRPs

Basalt fiber-reinforced polymers (BFRP) have recently become a widely used material for increasing the ultimate load capacity of reinforced concrete (RC) columns and improving their behavior throughout their entire life. So, an experimental program consisting of 19 large-scale RC columns of low strength has been prepared to examine the behavior of strengthened RC columns at all stages of loading. In this program, the columns were divided into two groups. The main parameters include the strengthening materials (BFRP wrap, BFRP bars, and carbon fiber reinforced polymers (CFRP) wrap), the strengthening techniques (fully or partially wrapping, the number and direction of layers), the cross-sectional shapes (square, rectangular, and circular), and the loading types (eccentric and concentric). The confinement of BFRP wraps significantly improved the service load and the ultimate failure load. The hybrid systems achieved an adequate level of confinement of the strengthened columns, increased the maximum load by 43.61 %, and absorbed energy by 477.2 % compared to the control column. BFRP bars used as a near-surface mounted material improved the behavior of the columns by up to 60 % and 411 % in maximum load and absorbed energy, respectively. The ACI and ECP codes predicted good estimates for the stress of columns strengthened with a single layer but underestimated for columns strengthened with a single layer and BFRP bars. Based upon the existing test data, BFRP-RC is recommended. Furthermore, this research can support using BFRPs as strengthening materials.

Neural network inspired differential evolution based task scheduling for cloud infrastructure

In recent years, cloud computing has become an essential technology for businesses and individuals alike. Task scheduling is a critical aspect of cloud computing that affects the performance and efficiency of cloud infrastructure. During this pandemic where most of the healthcare services like COVID-19 sampling, vaccination process, patient management and other services are dependent on cloud infrastructure. These services come with huge clients and server load in a small instance of time. These task loads can only be managed at cloud infrastructure where an efficient resource management algorithm plays an important role. The optimal utilization of cloud infrastructure and optimization algorithms plays a vital role. The cloud resources rely on the allocation policy of the tasks on cloud resources. Simple static, dynamic, and meta-heuristic techniques provide a solution but not the optimal solution. In such a scenario machine learning and evolutionary algorithms are only the solution. In this work, a hybrid model based on meta-heuristic technique and neural network is proposed. The presented neural network inspired differential evolution hybrid technique provides an optimal assignment of the tasks on cloud infrastructure. The performance of the DE-ANN hybrid approach is performed using performance metrics, average start time(ms), average finish time(ms), average execution time(ms), total completion time(ms), simulation time(ms), and average resource utilization respectively. The proposed DE-ANN approach is validated against BB-BC, and Genetic approaches. It outperforms the existing meta-heuristic techniques i.e. Genetic approach, and Big-Bang Big-Crunch. The performance is evaluated using two configuration scenarios using 5 virtual machines and 10 virtual machines with varying tasks from 1000 to 4500. Experimental results show that the DE-ANN technique significantly improves task scheduling performance compared to other traditional techniques. The technique achieves an average improvement of 19.15% in total completion time(ms), 32.23% in average finish time(ms), 51.95% in average execution time(ms), and 33.24% in average resource utilization respectively. The DE-ANN technique is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Strengthening of reinforced concrete beams by using FRPs-An overview

In recent years, the externally bonded reinforcing (EBR) method has been utilized to improve reinforced concrete (RC) constructions employing composites constructed of fiber reinforced polymers (FRP). Due to its strong nature, ideal reliability and suitability for use with concrete buildings when used, externally bonded Composites made of polymer with fibre reinforcement (FRP) are developing continually more popular in the construction sector for the treatment of degraded structures. This study's objective is to assess recent findings about the strengths, weaknesses, and potential of FRP enhanced constructions made of reinforced concrete (RC) under various situations for loading. In challenging environmental conditions that are static, dynamic, and environmental, FRP strengthening produces good results. Although it is yet unclear what causes debonding and FRP rupture when service loads and environmental exposures interact, these failure types are common. The acknowledgment of use of FRPs in enhancing RC constructions as techniques are developed for utilizing all of the material's strength, reducing brittleness and the danger of fires and unintentional damage decreasing usage of energy and carbon emissions the production process, and decreasing the material's significant upfront expense. The knowledge gaps according to the condition of the art are also identified in this report, along with possible FRP-enhanced structure research subjects that can advance knowledge and produce design standards.

Research on Insulator Defect Detection System Based on Edge Intelligence

The paper aims at the problems of the high occupancy rate of communication resources and heavy server load caused by the image data taken during the inspection of the substation and sent to the remote server for processing. An edge intelligent inspection data processing architecture is constructed, and an insulator defect detection model based on transfer learning and YOLO v5 is proposed. The model can meet the training of a small sample network model in the case of scarcity of power equipment defect data. The proposed algorithm is compared with typical target detection algorithms, and the experimental results show that the proposed algorithm has an excellent performance in model recognition accuracy and recall rate. The trained insulator defect detection model is further deployed on the developed edge intelligent terminal for testing, and its image processing speed can meet the image processing requirements for intelligent inspection of substations. The system has good application value.

Pola Kegagalan Balok Beton Bertulang Spiral Tanpa Beton Pada Penampang Tarik

Abstract The exploration of natural materials to produce concrete has an impact on environmental conditions and global warming that can cause natural disasters. Therefore, the use of concrete materials must be as efficient as possible. One solution to overcome this problem is to reduce the concrete in the tensile area and strengthen the beam using steel reinforcement in the support area. This study aims to determine the effect of tensile reinforcement on the behavior of beams without concrete in the tension area using a spiral system on crack patterns and beam patterns. The method used was an experimental method by making 2(two)specimens, a normal concrete beam (BN) as a control beam, and a spiral concrete beam without reinforcement in the tensile section with the addition of experimental reinforcement at the support section (BTRS 60D) where D is the diameter of the main reinforcement. The beams were designed as normal quality concrete beams with concrete quality of 45.07 MPa at the testing age of 28 days. The beam's dimension was 150 mm x 200 mm x 3100 mm. This study obtained the ultimate load on the BTRS specimen of 35.1 kN. While on the BN specimen, the ultimate load reached 47.65 kN. There was a decrease in BTRS service load by 27.57%. It is observed that the BN specimen experienced flexural cracks. The beam failure of the 2 test objects experienced beam failure that resembled under-reinforced where the reinforcing steel melted before the concrete was crushed. Keywords: spiral reinforcement, failure pattern, crack pattern.