Numerical investigation on the effects of local damage to the dynamic properties of buildings using limited vibration data

The challenge in structural health monitoring and damage detection is how to use the limited number of sensors on a building to assess the condition of the structure at any point in time. A case study simulating application of different levels of isolated local damage to the different floors were done to determine the corresponding dynamic properties and to observe the sensitivity of the dynamic properties to local changes using a several shear building models. Dynamic condensation was also applied to the model to portray limited vibration data.The results from the condensed 2 DOF model resulting into the two lowest natural frequencies in the structure can only be used in detecting the damage from the first unto the fifth floor in the building. With that, a change of 5% in the frequency can mean up to a 50% local damage to the stiffness in a single floor. These findings can be used to estimate the damage present in a building and provide warning signals for the building owner

Simulating size effect on shear strength of RC beams without stirrups using neural networks

An artificial neural network (ANN) model was developed using past experimental data on shear failure of slender RC beams without web reinforcements. The neural network model has five input nodes representing the concrete compressive strength (f′c), beam width ( b ), effective depth ( d ), shear span to depth ratio (a/d ), longitudinal steel ratio (ρ), five hidden layer nodes and one output node representing the ultimate shear strength (vu=Vu/bd). The model gives reasonable predictions of the ultimate shear stress and can simulate the size effect on ultimate shear stress at diagonal tension failure. The ANN model performs well when compared with existing empirical, theoretical and design code equations. Through the parametric studies using the ANN model, the effects of various parameters such as f′c, d, ρ and a d on the shear capacity of RC beams without web reinforcement was shown. This shows the versatility of ANNs in constructing relationships among multiple variables of complex physical processes using actual experimental data for training. © 2004 Elsevier Ltd. All rights reserved

Neural network modeling of shear strength of reinforced concrete beams

© 2005 EUCENTRE. All rights reserved. An artificial neural network (ANN) model was developed using past experimental data on shear failure of slender RC beams without web reinforcements. The neural network model has five input nodes representing the concrete compressive strength (f’c), beam width (b), effective depth (d), shear span-depth ratio (a/d), longitudinal steel ratio (p), five hidden layer nodes and one output node representing the ultimate shear strength (vu = Vu/bd). The model gives reasonable predictions of the ultimate shear stress and can simulate the size effect on ultimate shear stress at diagonal tension failure. The ANN model performs well when compared with existing empirical, theoretical and design code equations. Through the parametric studies using the ANN model, the effects of various parameters such as f’c, d U and a/d on the shear capacity of RC beams without web reinforcement was shown. This shows the versatility of ANNs in constructing relationships among multiple variables of complex physical processes using actual experimental data for training

An improved prediction model for bond strength of deformed bars in rc using upv test and artificial neural network

The composite action of reinforcement in the surrounding concrete involve a complex and non-linear mechanism.Inadequate understanding of the underlying interactions may lead to designs with insufficient amount of bond resistance of reinforcing bars in concrete structures.To investigate the effects of various parameters on the bond strength of steel bars in concrete, 54 cube samples with varying embedded reinforcements and strengths were prepared. The samples were cured for 28 days and tested using ultrasonic pulse velocity (UPV) test for sample homogeneity and single pull out test for bond strength.Data gathered in the experiment were used in the development of bond strength model as a function of compressive strength, concrete cover to rebar diameter ratio, embedment length, and UPV using artificial neural network (ANN). Of all the bond strength models considered from various literatures, the neural network model provided the most satisfactory prediction results in good agreement with the bond strength values obtained from the experiment. The UPV parameter was found to be one of the most significant predictors in the neural network model having a relative importance of 20.57%. This suggest that the robust prediction performance of the bond model was attributed to this essential component of the model. The proposed model of this study can be used as baseline information and rapid non-destructive assessment for zone wise strengthening in reinforced concrete. ©Int. J. of GEOMATE

A computer-aided semi-quantitative seismic risk assessment tool for safe school buildings

Risk assessment is the first step in making schools safer against seismic hazards like ground shaking, liquefaction, tsunami and landslide. It is important that such risk assessments involve the primary stakeholders such as school administrators and officials, but the tools utilized by the agency responsible for such assessments, the Department of Public Works and Highways (DPWH) are limited to screeners with a good background of engineering. Employing a semi-quantitative risk assessment that assesses qualitatively the school building\u27s assets, seismic hazards and vulnerabilities to the various hazards would allow the school administration and staff to participate in the decision-making process. A seismic risk index, defined as the product of these three factors categorizes the school buildings to be at low risk, medium risk or high risk to a specific seismic hazard. Through the computed indices, the school buildings in a specific compound are ranked and prioritized for further detailed inspections and possible repair or retrofitting. Mitigation procedures are recommended based on the identified vulnerabilities to reduce the risk. This paper presents the conceptual framework of the research and the semi-quantitative risk assessment methodology

Multi-hazard risk and asset value assessment of heritage buildings (Case study in Iloilo City, Philippines)

Heritage buildings belong to the most vulnerable class of structures because of the material degradation and the lack of structural design present. With the increasing frequency and magnitude of disasters, the need to preserve heritage buildings is further underlined. The risk assessment method considered various risks and the inclusion of the heritage building asset value. The pilot study in Iloilo was able to create a shortlist of prioritized heritage buildings for preservation

A Post-Disaster Functional Asset Value Index for School Buildings

© 2018 The Authors. Published by Elsevier Ltd. School buildings must be resilient during hazardous events like earthquakes so that the important functions of the school will not be affected. During disasters, schools have an added value and important function in post-disaster activities. Schools are often used as evacuation centers. When schools are damaged, the school\u27s mission of continuous delivery of education will be disrupted. Due to the inadequate number of structures in our country which are intended for evacuation during disasters, schools have been used in some cases as evacuation centers, again disrupting the school\u27s operations. To assure that school buildings will be operational in times of disaster, structural vulnerability assessment and appropriate retrofitting must be carried out. Due to budgetary constraints in most schools, a prioritization scheme must be devised to identify the buildings that must be given immediate attention. A rapid visual screening on the structural vulnerability due to earthquake hazards can be done and then rank the buildings for more detailed inspection and retrofitting. To refine the screening and ranking, the functional asset value of the buildings can be used as a second criterion. In a post-disaster scenario, school buildings have two important functional asset values: (a) Educational Functional Value and (b) Emergency Functional Value. The educational function focuses on continuous learning and consists of continuous conduct of classes, preservation of school records and documents for future use, and availability of basic resources and access to basic facilities. Emergency function focuses on protecting lives and consists of post-disaster uses of the school such as an evacuation center, storage of relief goods and an operation center. This study aims to develop a method of assigning an index corresponding to a school building\u27s post-disaster functional asset value using the Analytical Hierarchy Process (AHP) and an expert\u27s survey. Moreover, using the two-level vulnerability screening as a prioritization scheme, decision makers can prioritize the buildings that have high seismic risk and high functional asset value. This methodology was applied on a school campus as a case study

A multi-spatial assessment framework to geological hazard for high-rise building project in Metro Manila, Philippines

On Metro Manila Earthquake Impact Reduction Study (MMEIRS) conducted from 2002 to 2004 by Japan International Cooperation Agency (JICA) together with the Philippine Institute for Volcanology and Seismology (PHIVOLCS) and the Metro Manila Development Authority (MMDA), a worst-case scenario with a 7.2 earthquake magnitude in Makati, would have structural impacts on 50% of infrastructures from partial to heavy damage, 18.3% would be heavily damaged, 32.8% would be partially damaged, and major lifelines and utilities that provide electricity, telephones, and water could be cut off. There will be 2,300 casualties, 7,700 injuries, and 156,000 displacements. Geological hazard threatens the City residents and its attractiveness as the preferred residential and business location in the country. In this study, we model the earthquake risk of high-rise building projects in Global City, located at the eastern portion of Makati City near Marikina Valley Fault System. To accomplish this, we identified important geological hazard-related factors and distributed survey question to experts to gather estimation of the importance of each factor, and Monte Carlo Analytic Hierarchy Process (MCAHP) was used to determine the consistency of the expert\u27s judgment. These factor weights from the MCAHP are applied to the gathered data, and a Quantum GIS software tool was utilized for visualization, producing a geo-hazard map that is color-coded representing weighted-simulation levels of estimated earthquake hazard risks. © Published under licence by IOP Publishing Ltd