scholarly journals A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 463
Author(s):  
Sparsh Sharma ◽  
Suhaib Ahmed ◽  
Mohd Naseem ◽  
Waleed S. Alnumay ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Shear Strength ◽  
Geotechnical Engineering ◽  
Construction Cost ◽  
Cost Overrun ◽  
Risk Sensitive ◽  
Soil Shear Strength ◽  
External Forces ◽  
The Cost ◽  
Future Work

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

Research of Project Construction Cost Forecast Based on the Reliability

2014 ◽  
Vol 912-914 ◽  
pp. 1685-1689
Author(s):  
Yan E Hao
Keyword(s):  
Comparative Analysis ◽  
Probability Distribution ◽  
Historical Data ◽  
Construction Cost ◽  
Statistic Analysis ◽  
Actual Cost ◽  
Cost Overrun ◽  
Management Level ◽  
Project Construction ◽  
The Cost

The cost is one of the four major targets for the project management; this paper introduces the construction cost reliability as an indicator to measure the management level of project cost. Through the statistic analysis of historical data, the comparative analysis about the actual cost data and original planned cost for each unit of work in the finished project is conducted to confirm the probability distribution of cost overrun or cost saving and predict the reliability of cost, and this is applied in a real project case.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

Finite Element Modeling of a Mechanically Stabilized Earth Trial Wall

2021 ◽  
pp. 036119812110164
Author(s):  
Andrew Lees ◽  
Michael Dobie
Keyword(s):  
Finite Element ◽  
Shear Strength ◽  
Retaining Wall ◽  
Back Analysis ◽  
Soil Parameters ◽  
Failure Behavior ◽  
Valuable Insight ◽  
Deformation And Failure ◽  
First Case ◽  
Soil Shear Strength

Polymer geogrid reinforced soil retaining walls have become commonplace, with routine design generally carried out by limiting equilibrium methods. Finite element analysis (FEA) is becoming more widely used to assess the likely deformation behavior of these structures, although in many cases such analyses over-predict deformation compared with monitored structures. Back-analysis of unit tests and instrumented walls improves the techniques and models used in FEA to represent the soil fill, reinforcement and composite behavior caused by the stabilization effect of the geogrid apertures on the soil particles. This composite behavior is most representatively modeled as enhanced soil shear strength. The back-analysis of two test cases provides valuable insight into the benefits of this approach. In the first case, a unit cell was set up such that one side could yield thereby reaching the active earth pressure state. Using FEA a test without geogrid was modeled to help establish appropriate soil parameters. These parameters were then used to back-analyze a test with geogrid present. Simply using the tensile properties of the geogrid over-predicted the yield pressure but using an enhanced soil shear strength gave a satisfactory comparison with the measured result. In the second case a trial retaining wall was back-analyzed to investigate both deformation and failure, the failure induced by cutting the geogrid after construction using heated wires. The closest fit to the actual deformation and failure behavior was provided by using enhanced fill shear strength.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

scholarly journals The Analysis of Small Investors’ Demands on a Thermal Insulation System for a Family House: A Case Study

2021 ◽  
Vol 13 (5) ◽  
pp. 2491
Author(s):  
Alena Tažiková ◽  
Zuzana Struková ◽  
Mária Kozlovská
Keyword(s):  
Decision Making ◽  
Thermal Insulation ◽  
Construction Cost ◽  
Environmental Cost ◽  
Multi Criteria Decision Making ◽  
Insulation System ◽  
Insulation Systems ◽  
Small Investors ◽  
The Cost ◽  
Family House

This study deals with small investors’ demands on thermal insulation systems when choosing the most suitable solution for a family house. By 2050, seventy percent of current buildings, including residential buildings, are still expected to be in operation. To reach carbon neutrality, it is necessary to reduce operational energy consumption and thus reduce the related cost of building operations and the cost of the life cycle of buildings. One solution is to adapt envelopes of buildings by proper insulation solutions. To choose an optimal thermal insulation system that will reduce energy consumption of building, it is necessary to consider the environmental cost of insulation materials in addition to the construction cost of the materials. The environmental cost of a material depends on the carbon footprint from the initial origin of the material. This study presents the results of a multi-criteria decision-making analysis, where five different contractors set the evaluation criteria for selection of the optimal thermal insulation system. In their decision-making, they involved the requirements of small investors. The most common requirements were selected: the construction cost, the construction time (represented by the total man-hours), the thermal conductivity coefficient, the diffusion resistance factor, and the reaction to fire. The confidences of the criteria were then determined with the help of the pairwise comparison method. This was followed by multi-criteria decision-making using the method of index coefficients, also known as the method of basic variant. The multi-criteria decision-making included thermal insulation systems based on polystyrene, mineral wool, thermal insulation plaster, and aerogels’ nanotechnology. As a result, it was concluded that, currently, in Slovakia, small investors emphasize the cost of material and the coefficient of thermal conductivity and they do not care as much about the carbon footprint of the material manufacturing, the importance of which is mentioned in this study.

Shear Strength Monitoring System Based on Measurement of Shear Wave Velocity and Moisture Content

2014 ◽  
Vol 635-637 ◽  
pp. 750-754
Author(s):  
Peng Hu ◽  
Qing Li ◽  
Yi Wei Xu ◽  
Nan Ying Shentu ◽  
Quan Yuan Peng
Keyword(s):  
Shear Strength ◽  
Moisture Content ◽  
Shear Wave ◽  
Monitoring System ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Bender Elements ◽  
Strength Measurement ◽  
Soil Shear Strength ◽  
The Relationship

Expound the importance of soil shear strength measurement at mudslide hidden point to release the loss caused by the disaster, explain the relationship between shear wave velocity, moisture content and shear strength, design the shear strength monitoring system combining the shear wave velocity measured by Piezoelectric bender elements and moisture content.

scholarly journals Hospital Construction Cost Affecting Their Lifecycle: An Italian Overview

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 888
Author(s):  
Leopoldo Sdino ◽  
Andrea Brambilla ◽  
Marta Dell’Ovo ◽  
Benedetta Sdino ◽  
Stefano Capolongo
Keyword(s):  
Healthcare Services ◽  
Construction Cost ◽  
Design Stage ◽  
Quality Healthcare ◽  
Healthcare Facilities ◽  
Early Design Stage ◽  
Hospital Facility ◽  
Italian Context ◽  
The Cost ◽  
Cost Components

The need for 24/7 operation, and the increasing requests of high-quality healthcare services contribute to framing healthcare facilities as a complex topic, also due to the changing and challenging environment and huge impact on the community. Due to its complexity, it is difficult to properly estimate the construction cost in a preliminary phase where easy-to-use parameters are often necessary. Therefore, this paper aims to provide an overview of the issue with reference to the Italian context and proposes an estimation framework for analyzing hospital facilities’ construction cost. First, contributions from literature reviews and 14 case studies were analyzed to identify specific cost components. Then, a questionnaire was administered to construction companies and experts in the field to obtain data coming from practical and real cases. The results obtained from all of the contributions are an overview of the construction cost components. Starting from the data collected and analyzed, a preliminary estimation tool is proposed to identify the minimum and maximum variation in the cost when programming the construction of a hospital, starting from the feasibility phase or the early design stage. The framework involves different factors, such as the number of beds, complexity, typology, localization, technology degree and the type of maintenance and management techniques. This study explores the several elements that compose the cost of a hospital facility and highlights future developments including maintenance and management costs during hospital facilities’ lifecycle.

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