This paper provides a review of the development of digital fabrication methods in the construction industry and academia, with a major emphasis on extrusion-based 3D printing. A detailed description of the work done by the research group in IIT Madras in developing a Portland cement-based 3D printable formulation is discussed. Finally, the areas in digital fabrication that require further research and attention are also highlighted in this paper.
The purpose of this study is to bring out the factors affecting labour productivity, which can have a strong influence on both time and cost of construction of precast concrete buildings in the developing world countries such as India. This paper also provides a brief discussion on some parameters affecting the functionality of precast concrete buildings. For identifying the ‘wastes’ in the processes and quantifying the shortcomings in the productivity, work sampling was conducted in five precast concrete construction (PCC) projects in India. Freewheeling, on-site interviews were conducted at the projects to find out the factors affecting productivity. The frequency and severity of the various parameters affecting the functionality of precast concrete buildings were obtained through a questionnaire survey. The work sampling analysis found that about 46% were NVA (Non-value adding) and NVAR (Non-value adding but required) activities, which adversely affected the productivity and that the process ‘wastes’ identified at erection sites were more than those in the production yards. The questionnaire survey on functionality revealed that ‘non-conformance with tolerance limits for precast concrete elements’ has ‘high’ frequency of occurrence and ‘very high’ severity and a field study found that about 40% of the precast panels failed to comply with tolerance limits. The reasons for ‘wastes’ in different trades viz. concreting, shuttering, reinforcement and erection in Indian PCC projects were analyzed in this paper which could help in understanding the reasons behind the prevailing low productivity. More importantly, this study also explores the parameters affecting the functionality of precast concrete buildings.
While the functionality issues identified may or may not be typical of the entire precasting industry, the wastage issues identified have been widely reported in literature as quite prevalent in the construction industry.
Along with the research advancement in the field of nanotechnology, the sensor used in construction industry has also become smart which are designated as self-sensing materials. For the smart cities to be really smarter, these self-sensing materials are proving more helpful, as they monitor structural health of the civil engineering infrastructures.
These smart materials include smart concrete, which imparts any concrete structure the abilities of self-sensing. Here selfsensing concrete (SSC) means a concrete as a structural material having properties to monitor itself the stress, strain, crack etc. without the support of embedded, attached or remote sensors. This self sensing concrete establishes the relationship between changes in internal strain and changes in corresponding material properties like electrical resistance or temperature. These sensors are having more advantages of cost and sensing size, over the conventional structural health monitoring (SHM) sensors available in the market as it provides more durability combined with uniformly distributed measurements. This paper takes a review of the recent advancements in sensors made up of cementitious matrix used along with using Carbon Nanotubes (CNT’s). Also, experimental work is carried out to study the electro-mechanical behavior of the sensor when subjected to loads.
Technology development and adoption in the construction sector are slow. The traditional approach to product development has been inefficient and unproductive in terms of commercialisation. On the other hand, management by process rather than function with a multi-functional project team approach has recently gained wide acceptance. Such an innovation process can fasttrack new product development and technology adoption. This article discusses an Innovation Process Model adopted from the well-known Software Development Life Cycle (SDLC) process to fast-track technology development in the field of construction materials. The application of the model is illustrated with several real-life case studies, e.g., semi-flexible extruded ECC pipe, geopolymer, and ultra-high volume flyash concrete.
High strength concrete (HSC) columns, with strengths up to 100MPa, are increasingly used in multi-storey buildings with flat slabs due to various advantages offered by them. However, for reasons of economy, only normal strength concrete is used in slabs. The two different concrete mixtures encountered at such slab-column joints pose a design and construction problem. Though the Indian code is silent on this aspect, ACI code was the first to suggest three different approaches. Of these, the third approach of designing the slab-column joint using an effective concrete strength, fce , is found to be reasonable and easy to follow. The available formulae suggested by several researchers and also found in the ACI, Canadian and Australian codes to predict the effective concrete strength of corner/edge column joints are reviewed. An equation, similar in format to that of an earlier equation recommended for interior column joints is recommended for edge/corner joints. It is found that this equation predicts the effective concrete strength reasonably as compared to the existing experimental results, even for UHSC and also when the ratio of the column concrete strength to the slab concrete strength ( fc'c /fc's ) is as high as 4.0. This formula may be adopted in the future version of codes.
In this paper, the morphological characteristics of rollercompacted concrete (RCC) mixes containing reclaimed asphalt pavement (RAP) aggregates was studied using a scanning electron microscope (SEM). The SEM investigation was performed on the fractured RCC specimens containing 50% RAP (denoted as 50RAP mix). SEM investigation validated that the asphalt-cohesion failure in the case of RAP-concrete mixes will be predominant in reducing the mechanical strength even though additional calcium silicate hydrate (C-S-H) gels are provided by the fly ash particles. Meanwhile, the morphology of the 50RAP mix is almost similar to that of the conventional concrete, except that it consists of two types of interfacial transition zone (ITZ): a normal ITZ occurring between the natural aggregate and the mortar while another ITZ occurring between the RAP and the mortar. Hydrated C-S-H phases with fibrous, needle-like, reticulated, small discs, and tapered fibers morphologies were also observed in the SEM micrographs of the 50RAP mix. The highly porous ITZ of the RAP-concrete relative to the ITZ of the conventional-concrete has a negative impact on the strength of the RAP-concrete but the softening of asphalt at higher temperature clogs the empty pores and lowers the porosity of the concrete. The presence of needlelike ettringites were also identified in the mix containing RAP which may also be one reason for causing expansion in the RAP-concrete. In a nutshell, the utilization of RAP in RCC should be limited to paving applications only rather than in structural concrete.
Defect estimation of concrete structures by machine learning of spectrogram images of impact echo sound has been shown to be as accurate as human expert. To improve the accuracy of the estimation and to confirm the generalization of the estimation, a modification was examined in this paper. For this purpose, impact echo test was performed on a real offshore concrete structure. Here, machine learning was performed using the difference of spectrogram images between the reference and training data under the assumption that the change in impact-echo due to deterioration was constant irrespective of the object, the estimation results were compared with the estimation by a human expert. Also, in order to evaluate the generalization performance of estimation method by machine learning of spectrogram images, estimation with test data with different conditions from the training data was executed. As a result, although the learning by difference or average of spectrogram images did not show the higher accuracy than the conventional method. However, a certain level of generalization capacity was observed of this method.
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