The construction industry is changing constantly and becoming more complex. It requires new strategies for compliance with national and international scenarios. Developing each project is associated with many limitations, including time, cost, changes, wastes, and errors, which are often not avoidable. Due to numerous project stages and complexities in the construction industry, usually, different mistakes and duplications occur. Meanwhile, Building Information Modeling (BIM) has created one of the most important and essential changes in this industry and results in more in-depth cooperation among project stakeholders. BIM is one of the most recent innovations in the construction industry, which resolves the problems of projects faster. BIM can be applied by architects, engineers, contractors, project managers, etc. to achieve objectives such as reducing design errors, reducing time and cost, improving design and construction integration, and increasing coordination and cooperation among different sections. Given the significance of project success in every country and several problems in each project, using BIM is an appropriate solution, which its proper implementation requires understanding its benefits that is the main aim of this study. This research identifies and classifies these benefits through the Systematic Literature Review (SLR) method, describing the significance of using BIM in infrastructure projects.
health-related problems and even death among animals and human beings. Agriculture is the main food source; thus, many interventions are made such as that of irrigation by the local county and national government initiated through the National Irrigation Board (NIB). Despite the irrigation projects food insufficiency still persists, therefore their sustainability is questionable. One such approach to improving the sustainability of irrigation projects is participatory monitoring and evaluation which leads to ownership and then higher sustainability. In the study, the objective was to asses if taking corrective action after participatory monitoring and evaluation (PME) influence project sustainability. The study used a descriptive survey and correlation designs to collect data from 316 respondents selected using stratification sand purposeful with strict randomization. Questionnaires were administered and interviews were conducted on selected sample respondents on appointed dates. Data were analyzed using SPSS version 25.0 to get descriptive statistics, correlations coefficients were obtained to test association and degree of strength. Testing of the hypothesis was done using linear regression. The study findings were that a large number of respondents were between ages 31 to 40 years and most were female with their highest level of education being primary school. The influence of PME capacity building on the dependent variable and irrigation projects sustainability found that the farmers were not taken for exposure visits and project officers were not accountable for money use. Age, gender, and education level have very minimal influence on PME capacity building. PME capacity building had a weak positive influence of r = 0.290 and it explained only 8.4% of irrigation projects sustainability in Kitui County. The study recommends that to improve project capacity building: project revenue must be controlled on use, farmers must be taken for exposure visits to learn from successors, project officers should be accountable for funds use, and project guidelines should be improved to increase sustainability. Implementation of these recommendations will reduce the loss of Arid and Semi-Srid Lands (ASALs) and attain higher and longer sustainability in food projects, thus, reducing the recurrence rate of food shortage, improve and hasten the implementation of irrigation projects, show the need to involve primary stakeholders in project monitoring and appraisal for sustainability, better and efficient decisions by policymakers to increase chances of project’s success.
The construction project being studied is a government investment related to the relocation of a biomedical institute delivering research-based knowledge and contingency support in the fields of animal health, fish health and food safety. The project covers a total of 63,000 square meters distributed over 10 buildings with a very high degree of complexity. The design alone has required 1 million hours, which relates to a client cost of about 100 million Euro. The purpose of this paper is to study the applied methodology for managing the detailed design to identify lessons learned from the project. The theory underlying the study is inspired by lean design management and design theory linked to design as phenomena, including reciprocal interdependencies, iteration, decomposition, design as a “wicked problem”, learning, gradual maturation, etc. The article is based on an abductive research design and has been implemented as a case study where both qualitative and quantitative methods have been used.
Construction projects are much appreciated by both client and contractor when completed on schedule and within budget so as to avoid cost overruns. The Zambian building sector normally experiences time and cost overruns. This study investigated the feasibility of using tilt-up construction in the construction of commercial building walls. The methodology used consisted of a literature review, a questionnaire survey and a scenario analysis consisting of a hypothetical 4900 square meter commercial building with a height of 8 meters. Sixty-six questionnaires were administered to design professionals operating in the Zambian building sector using simple random sampling and thirty-six were returned giving a response rate of 55%. The data were analyzed using descriptive statistics. Cost analysis was done on a hypothetical building as no contractor was found using tilt-up construction in the construction sector. The study established that tilt-up was, in fact, more expensive than the conventional methods (concrete blocks and in-situ reinforced concrete walls), but it was faster, hence, making it viable in respect to time and not cost in the Zambian construction industry (ZCI). Additionally, necessary expertise was available with the exception of a certified tilt-up practitioner and a sealant sub-contractor in cases where a sealant contractor is needed. The study has identified that currently in the Zambian building sector tilt-up construction can be used when time is more important than the cost. However, challenges such as site size (limited space), the unavailability of building regulation for tilt-up construction and the economic capacity of the client or capacity need to be addressed for enhancing the practical application of tilt-up construction in ZCI.
This study tries to investigate project success through inclusive leadership role along with self-efficacy. Data sets were collected using adopted questionnaires of previous studies from employees working on the metro bus project, their supervisors and passengers of metro bus service from Rawalpindi to Islamabad route in Pakistan. This study is measuring the effects of inclusive leadership on project success through self-efficacy which makes it causal in nature. The time lag data collection method was adopted. In order to reach correct findings, potential biases were controlled by theoretical and statistical controls. Exploratory factor analysis was used to test structural modelling, average variance and composite reliabilities using Smart PLS. SPSS 21.0 was used for regression analysis, bias correction measures were also considered. The study revealed that inclusive leadership is associated in a positive manner with project success. The mediating role of self-efficacy in the relationship of inclusive leadership and project success was also supported. In addition, theoretical and practical implications in the context of this study are discussed in detail.
Integrated Project Delivery (IPD) is introduced as a new delivery system that fosters high efficiency by delivering accurate information and new technologies in a collaborative team environment. In this sense, the research aimed to review the IPD principles and their main categories, such as contract, process, information & modeling (I&M), team and communication as well as perform a qualitative analysis to illustrate the current research trends. The qualitative analysis performed was made through a series of collected articles from 2001 to 2018 in 08 different scientific database websites. In terms of the results, the contract category illustrated a strong trend, where the studies are focus on collaborations and frameworks to enhance high efficiency in construction. In the I&M category, demonstrated an increasing trend applying the Building Information & Modeling (BIM) subject as well as team category, where showed the importance of a wellstructured team and their impact on the project., The process and communication categories illustrated a weak trend, allowing opportunities in the field. Finally, the current study reviewed and analyzed the IPD and its main categories allowing a solid basis for future research.
Voice recognition technology has been in existence over several decades but its application in the construction industry has been minimal. Despite the several advantages it offers, its application has been limited to smart building integration only. This study has made a significant contribution by integrating voice recognition technology into key-in building quantities estimation software. The Visual Basic programming language was used to design and code the interface of the voice recognition system and key-in estimating software model. The prototype model continues to have some challenges because it cannot work efficiently in a noisy work environment and there is limited range of vocabulary it can recognize. This paper seeks to challenge the stakeholders of the construction industry to maximize the benefits of voice recognition technology and integrate it into other construction tasks. In addition, future research can consider integrating building information modeling and voice recognition technology.
This paper presents the ARQUIGRAFIA project, an open, public and nonprofit, continuous growth web collaborative environment dedicated to Brazilian architectural photographic images.
The ARQUIGRAFIA project promotes the active and collaborative participation among its institutional users (GLAMs, NGOs, laboratories and research groups) and private users (students, professionals, professors, researchers), both can create an account and share their digitized iconographic collections in the same Web environment by uploading their files, indexing, georeferencing and assigning a Creative Commons license.
The development of users interactions by means of semantic differentials impressions recording on visible plastic-spatial aspects of the architectures in synthetic infographics, as well as by the retrieval of images through an advanced system search based on those impressions parameters. By gamification means, the system often invites users to review images’ in order to improve images’ data accuracy. The pilot project named Open Air Museum that allows users to add audio descriptions to images in situ. An interface for users’ digital curatorship will be soon available.
The ARQUIGRAFIA’s multidisciplinary team gathering professors-researchers, graduate and undergraduate students from the Architecture and Urbanism, Design, Information Science, Computer Science faculties of the University of São Paulo, demands continuous financial resources for grants, for contracting third party services, for the participation in scientific events in Brazil and abroad, and for equipment. Since 2016, significant budget cuts in the University of São Paulo own research funds and in Brazilian federal scientific agencies can compromise the continuity of this project.
The open source template called +GRAFIA that can freely help other areas of knowledge to build their own visual Web collaborative environments.
The collaborative nature of the ARQUIGRAFIA distinguishes it from institutional image databases on the internet, precisely because it involves a heterogeneous network of collaborators.
With more and more digital collections of various information resources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of DDC.
State-of-the-art machine learning algorithms require at least 1,000 training examples per class. The complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data (totaling 802 classes in the training and testing sample, out of 14,413 classes at all levels).
Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average; the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the task. Word embeddings combined with different types of neural networks (simple linear network, standard neural network, 1D convolutional neural network, and recurrent neural network) produced worse results than Support Vector Machine, but reach close results, with the benefit of a smaller representation size. Impact of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as input. Stemming only marginally improves the results. Removed stop-words reduced accuracy in most cases, while removing less frequent words increased it marginally. The greatest impact is produced by the number of training examples: 81.90% accuracy on the training set is achieved when at least 1,000 records per class are available in the training set, and 66.13% when too few records (often less than 100 per class) on which to train are available—and these hold only for top 3 hierarchical levels (803 instead of 14,413 classes).
Having to reduce the number of hierarchical levels to top three levels of DDC because of the lack of training data for all classes, skews the results so that they work in experimental conditions but barely for end users in operational retrieval systems.
In conclusion, for operative information retrieval systems applying purely automatic DDC does not work, either using machine learning (because of the lack of training data for the large number of DDC classes) or using string-matching algorithm (because DDC characteristics perform well for automatic classification only in a small number of classes). Over time, more training examples may become available, and DDC may be enriched with synonyms in order to enhance accuracy of automatic classification which may also benefit information retrieval performance based on DDC. In order for quality information services to reach the objective of highest possible precision and recall, automatic classification should never be implemented on its own; instead, machine-aided indexing that combines the efficiency of automatic suggestions with quality of human decisions at the final stage should be the way for the future.
The study explored machine learning on a large classification system of over 14,000 classes which is used in operational information retrieval systems. Due to lack of sufficient training data across the entire set of classes, an approach complementing machine learning, that of string matching, was applied. This combination should be explored further since it provides the potential for real-life applications with large target classification systems.