EVALUASI KINERJA ASET FASILITAS REKREASI DI TERAS CIKAPUNDUNG KOTA BANDUNG

ABSTRACTOne of the city parks designed as a recreation area is Teras Cikapundung. Teras Cikapundung has an area of 5,128 m2 and located in the Siliwangi area of Bandung City. Based on the results of preliminary observations have found several indications of problems with existing facilities in the Teras Cikapundung recreation area, namely non-operational children's rides, unavailability of parking spaces for bicycles, the stairs are not equipped with handrails, some of park chairs, picnic tables, and garden lights can’t be used because in the damage condition, and there is only two kind of signage: direction and information. The purpose of this case study research is to determine the performance of facility assets based Recreational Facility Audit Tool and provide solutions or recommendations for problems in recreational facilities in Teras Cikapundung. The theory for this research based on 'The Recreational Facility Audit Tool' from Macfarlane et al (2013) includes availability of sport facilities, accessibility, and availability of supportive amenities. The research method is descriptive method with qualitative and quantitative research approaches. The data collection techniques were observation, interview, and documentation study. The analysis techniques is using descriptive analysis techniques. Therefore, the recommended problem solving recommendation is the renovation of recreational facilities assets in Teras Cikapundung Bandung City.Keywords: Asset Performance Evaluation, City Park, Recreational FacilitiesABSTRAKSalah satu taman kota yang dirancang sebagai tempat rekreasi taman kota adalah Teras Cikapundung. Teras Cikapundung memiliki luas sebesar 5.128 m2 dan berlokasi di daerah Siliwangi Kota Bandung. Berdasarkan hasil observasi pendahuluan ditemukan beberapa indikasi masalah pada fasilitas yang ada pada kawasan rekreasi Teras Cikapundung yaitu permainan anak yang tidak beroperasi, tidak tersedianya tempat parkir untuk sepeda, tangga yang disediakan tidak dilengkapi oleh pegangan, selain itu beberapa fasilitas kursi taman, meja piknik, dan lampu taman tidak dapat digunakan karena dalam kondisi rusak, serta hanya tersedia 2 jenis rambu yaitu rambu petunjuk arah dan informasi. Tujuan penelitian studi kasus ini untuk mengetahui kinerja aset fasilitas berdasarkan Recreational Facility Audit Tool dan memberikan solusi atau rekomendasi atas permasalahan yang terdapat pada fasilitas rekreasi di Teras Cikapundung. Teori yang digunakan pada penelitian ini adalah ‘The Recreational Facility Audit Tool’ dari Macfarlane et al (2013) meliputi availability of sport facilities, accessibility, dan availability of supportive amenities. Metode penelitian yang digunakan adalah metode deskriptif dengan pendekatan penelitian kualitatif dan kuantitatif. Teknik pengumpulan data yang digunakan yaitu observasi, wawancara, dan studi dokumentasi. Teknik analisis yang digunakan adalah teknik analisis deskriptif. Rekomendasi pemecahan masalah yang disarankan dalam penelitian ini adalah renovasi aset fasilitas rekreasi di Teras Cikapundung Kota Bandung.Kata kunci : Evaluasi Kinerja Aset, Fasilitas Rekreasi, Taman Kota

A Thermodynamically Consistent Model of Quasibrittle Elastic Damaged Materials Based on a Novel Helmholtz Potential and Dissipation Function

In the paper, a thermodynamically consistent model of elastic damaged material in the framework of small strain theory is formulated, describing the process of deterioration in quasibrittle materials, concrete in particular. The main goal is to appropriately depict the distinction between material responses in tension and compression. A novel Helmholtz energy and a dissipation potential including three damage parameters are introduced. The Helmholtz function has a continuous first derivative with respect to strain tensor. Based on the assumed functions, the strain–stress relationship, the damage condition, the evolution laws, and the tangent stiffness tensor are derived. The model’s predictions for uniaxial tension, uniaxial compression, uniaxial cyclic compression–tension, and pure shear tests are calculated using Wolfram Mathematica in order to identify the main features of the model and to grasp the physical meaning of an isotropic damage parameter, a tensile damage parameter, and a compressive damage parameter. Their values can be directly bound to changes of secant stiffness and generalized Poisson’s ratio. An interpretation of damage parameters in association with three mechanisms of damage is given. The considered dissipation potential allows a flexible choice of a damage condition. The influence of material parameters included in dissipation function on damage mode interaction is discussed.

Multisystem Inflammatory Syndrome in Children (MIS-C) Following SARS-CoV-2 Infection: Role of Oxidative Stress

With the appearance of the SARS-CoV-2 virus in December 2019, all countries in the world have implemented different strategies to prevent its spread and to intensively search for effective treatments. Initially, severe cases of the disease were considered in adult patients; however, cases of older school-age children and adolescents who presented fever, hypotension, severe abdominal pain and cardiac dysfunction, positive for SARS-CoV-2 infection, have been reported, with increased pro-inflammatory cytokines and tissue damage, condition denominated multisystemic inflammatory syndrome (MIS-C); The emerging data from patients with MIS-C have suggested unique characteristics in the immunological response and also clinical similarities with other inflammatory syndromes, which can support as a reference in the search for molecular mechanisms involved in MIS-C. We here in propose that oxidative stress (OE) may play a very important role in the pathophysiology of MIS-C, such as occurs in Kawasaki disease (KD), severe COVID-19 in adults and other processes with characteristics of vascular damage similar to MIS- C, for which we review the available information that can be correlated with possible redox mechanisms.

Nondestructive Testing Based Compressive Bearing Capacity Prediction Method for Damaged Wood Components of Ancient Timber Buildings

In this research, a wave-drag modulus nondestructive testing method was proposed to predict the compressive bearing capacity of damaged wood components. Using an ancient Chinese building as a case study, internal and external inspections were performed to obtain defect data and related tree species information. Using the same tree species, wave-drag modulus and scale tests were carried out to predict the residual bearing capacity when there was damage in the form of internal cavities or edge material reduction and to compare the damage and loss experimental data. The results show that the internal defect combination model established by two nondestructive testing methods (stress wave and impedance meter) based on the weight distribution can accurately determine the internal damage condition of wood components. There was a significant correlation between wave-drag modulus and compressive strength along the wood grains. The measured values of wood components with different defects were consistent with the theoretical values predicted by the wave-drag modulus, which can effectively improve the prediction of residual bearing capacity. In addition, it was determined that edge material reduction is more destructive to a wood component than the presence of an interior cavity. Thus, the wave-drag modulus can quickly locate vulnerable sections and provide a relevant basis for judging the material condition of wood components in ancient buildings.

Identification of cavitation in centrifugal pump by artificial immune network

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

Research on the Inspection Method of Chimney Appearance and Masonry Material Performance

As a high-rise structure, the safety of chimneys has always been a public concern. In this paper, the damage condition, tilting, and the strength of load-bearing materials of the chimney were inspected, and the inspection conclusions and maintenance suggestions were given based on the inspection results. The inspection method can provide relevant reference for the inspection of similar structures.

Decision-making system and verification of pavement diseases treatment scheme for highway reconstruction and extension

Based on the case of a highway reconstruction and extension project in Guangdong Province, a decision-making system for the treatment of the old pavement diseases of semi-rigid base asphalt pavement's reconstruction and extension has been established. The load-carrying capacity of the old pavement structure has been obtained by the Benkleman beam deflectometer; the damage condition of the road surface has been determined by investigation of road surface diseases; the restorability of the pavement diseases has been evaluated by investigation of the pavement maintenance history; the interior damage condition of the pavement has been detected by the three-dimensional ground-penetrating radar; furthermore, the decision-making system of the old pavement damage treatment scheme has been constructed and the treatment scheme of old pavement diseases has been put forward by using the four-hierarchy indexes of ‘the load-carrying capacity + the condition of road surface + the maintenance history + the interior damage condition of the pavement’; and the treatment effect of pavement diseases has been verified by bearing capacity test. The results show that the variation level of the deflection representative value of the old pavement has been reduced by about 8.2% ∼ 25.1% by adopting different diseases treatment schemes in different sections; 44.2% by adopting the deep grouting of the roadbed scheme, and 61.6% effectively by using the plan of milling all the asphalt layers and the upper base, and then relaying the C20 plain concrete to the top of the upper base and relaying asphalt mixture. The decision-making system for the treatment of the old pavement diseases of the semi-rigid base asphalt pavement's reconstruction and extension can effectively guide the treatment of old pavement disease, notably improve the load-carrying capacity of old pavement structure and the balance of the load-bearing capacity in different sections of the pavement structure.

Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)

Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

A deep network-based model of hippocampal memory functions under normal and Alzheimer’s disease conditions

We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: 1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and 2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer’s disease (AD) conditions. The network is trained on pictures and names of digits from 0 – 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words (“odd” instead of “nine”). Under severe damage conditions, the network shows a null response (“I don’t know”). Neurobiological plausibility of the model is extensively discussed.