Decrease Labor Costs Research Articles

Printable Hollow Concrete Beams by 3D Concrete Printer

3D printers are constantly developing and have a wide range of uses. Today, it is actively used in many fields, from the production of implants and prostheses to the production of jewelry. This technology, which is widely used in civil engineering in areas such as building element production, sustainability, and building construction offers several advantages over conventional concrete pouring and processing methods. Decreasing material waste, decreasing labor costs, freedom and originality in design, and saving time are among these advantages. In this context, 3DCP compared to traditional methods, it reduces the use of materials and makes the construction process more efficient since it is designed with different internal patterns and spaces. This study focuses on the printing process of hollow concrete beam designs in 3 different patterns with 2 different mixtures. Crushing between the layers of beams printed with 3D concrete printing, layer height, and printability and fresh state tests of the mixtures were examined.

Benchmarking analysis of computer vision algorithms on edge devices for the real-time detection of digital dermatitis in dairy cows

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the coronary band of the foot. It causes significant animal welfare and economic losses to the cattle industry. Early detection of DD can lead to prompt treatment and decrease lameness. Current detection and staging methods require a trained individual to evaluate the interdigital space on each foot for clinical signs of DD. Computer vision (CV), a type of artificial intelligence for image analysis, has demonstrated promising results on object detection tasks. However, farms require robust solutions that can be deployed in harsh conditions including dust, debris, humidity, precipitation, other equipment issues. The study aims to train, deploy, and benchmark DD detection models on edge devices. Images were collected from commercial dairy farms with the camera facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system. Models were trained to detect and score DD lesions and embedded on an edge device. The Tiny YOLOv4 model deployed on a CV specific integrated camera module connected to a single board computer achieved a mean average precision (mAP) of 0.895, an overall prediction accuracy of 0.873, and a Cohen’s kappa of 0.830 for agreement between the computer vision model and the trained investigator. The model reached a final inference speed of 40 frames per second (FPS) and ran stably without any interruptions. The CV model was able to detect DD lesions on an edge device with high performance and speed. The CV tool can be used for early detection and prompt treatment of DD in dairy cows. Real-time detection of DD on edge device will improve health outcomes, while simultaneously decreasing labor costs. We demonstrate that the deployed model can be a low-power and portable solution for real-time detection of DD on dairy farms. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time detection of health outcomes in precision farming.

Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0

Anomaly detection for industrial diesel generators, in which unexpected faults could lead to severe consequences, is still challenged due to their complex structure and nonstationary operation. Maintenance engineers who manually audit diesel generators for anomaly detection require significant expertise and knowledge. This study proposes a real-time intelligent AIoT system-based convolution neural network long short-term memory (CNN-LSTM) to enhance efficiency and decrease labor costs of industrial diesel generator maintenance service. The AIoT system could autonomously classify abnormal conditions of industrial diesel generators through supervised learning techniques. Several anomaly failure conditions are identified by maintenance experts and are simulated in the laboratory to collect the working parameters based on developed IoT modules. Pearson product-moment coefficients are computed to effectively evaluate the interdependence between collected variables and the target anomaly types. The proposed CNN-LSTM structure is hyperparameter fine-tuning for identifying the most critical configurations in failure-diagnosing applications. The developed approach is comprehensively analyzed and evaluated with other state-of-the-art individual deep learning algorithms, including recurrent neural network (RNN), LSTM, gate-recurrent unit (GRU), and CNN. The experiment results indicate that the proposed hybrid CNN-LSTM could achieve distinguished diagnosis precision of anomaly conditions of industrial diesel generators and significantly improve the classified performance in Industry 4.0.

Comparison of robot concepts for new sustainable crop production systems

In recent decades the agricultural intensification led to a landscape simplification, where once heterogeneous landscapes turned into more monocultured cropping regions. The main driver behind it was to be able to use larger and more powerful agricultural machinery in order to increase productivity and specially to decrease labor costs. With the advent of robots in combination with new methods of AI it is obvious that this is not only a new step of automated mechanization. New robotic systems open up new opportunities of how to cultivate crop plants. Different robotic concepts with different properties are discussed and how these might contribute to achieve the indisputably needed improvements in sustainable and environmental-friendly crop production systems. In a performed case study, it is demonstrated that even if larger robotic solutions when operated in isolation still can achieve higher area outputs, smaller and often specialized robotic systems can achieve a substantially improved work quality in the use case of seeding.

Locating charging stations and routing drones for efficient automated stocktaking

Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighborhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.

Design and Fabrication of Automatic Weight, Color and Height Based Sorting System

Sorting is a necessary task that is frequently performed in daily life. Manual sorting is an expensive, difficult, and drawn-out operation. Additionally, it slows down manufacturing operations. To solve these issues the sorting system now uses Low-Cost Automation which aims to decrease labor costs, manufacturing times, and processing complexity while also enhancing product quality, boosting output, etc. In this research, an efficient technique for automatically sorting the object based on color, height, and weight has been devised. The objects are recognized, separated, and collected using a collection bin along with a conveyor belt, strain gauge load cell, TCS34725 RGB color sensor, ultrasonic sensor, extra channel path, etc. according to their colors. The Arduino Uno controls the entire operation of this system. Here, three types of products are sorted based on their weight, color, and height. These three colors are red, blue, and green. Firstly, the weight is measured using strain gauge load cells. Then the color of the product has been detected by the color sensor at the same stage, and according to the color collecting bin rotates to the desired angle. Finally, the height is measured by an ultrasonic sensor. An extra channel is used at the end of the conveyor belt. At the end of the channel, a three-portion collecting bin is kept. The bin can be rotated at the desired angle by a servo motor after detecting the specified color object, and objects are being collected from this bin. Thus, the objects are sorted out based on their weight, color, and height. The experimental results show that the system fits the design requirements, is dependable, and has high sorting accuracy.

Design and Fabrication of an Agricultural Spraying Machine Using a Knapsack Sprayer

Plant protection equipment is essential in the agricultural field for increasing crop productivity and minimizing the losses caused. Specialized machinery for mechanically applying chemicals is crucial for maximizing crop yields in farming. Farmers use a wide range of pesticides to counteract the effects of insects, diseases, and fungi on their crops. The backpack sprayers do the spraying with staffing, which requires more human effort, covers a small area, is time-consuming, and has a low storage capacity. The market introduced many products to address these limitations, but they cannot work simultaneously. Solar, electrical, and chemical energy is now being used to spray pesticides, but these methods are expensive and require a trained operator. To address the issues above, a product designed with adaptable spraying equipment is proposed to benefit medium-and small-scale farmers. This study aims to look at the problem of manual backpack sprayers and reduce backpack and foot spraying, eliminate human efforts, and decrease labor costs by advancing the spraying method and a constant flow of droplets. Finally, we have investigated the effects of urging gardeners and farmers to spray their plants with liquids such as pesticides, herbicides, and water. The proposed design can help gardeners in terms of comfort during spraying, reducing energy used to pump tanks, and effectively utilizing spraying time. IUBAT Review—A Multidisciplinary Academic Journal, 6 (1): 88-99

Toward the internet of things forensics: A data analytics perspective

AbstractThe widespread use of networked, intelligent, and adaptable devices in various domains, such as smart cities and home automation, climate control, manufacturing and logistics, healthcare, education, and agriculture, has been hastened by recent developments in hardware and software technologies. In all these application domains, the concept of the Internet of Things helps to achieve process automation and decrease labor costs. While IoT has been an established domain for quite a while, it has seen a lot of advances and challenges in different subdomains over the years. One such subdomain is IoT Forensics which involves digital forensics concerning IoT devices, networks, or clouds. In this process of obtaining substantial evidence from the devices, networks, or cloud, a large amount of data and operations on said data are involved. Hence, looking through IoT Forensics through the methodology dealing with data, known as data analytics, is essential. This paper presents an interpretation of IoT Forensics from the standpoint of data analytics. To explain the same in detail, the paper focuses on IoT Forensics, its methodologies, and how they relate to data analytics stages. Toward the end, the paper discusses current developments in IoT Forensics from the data analytics perspective, limitations observed in the existing technologies, adoption challenges, and possible future advancements.

The Effects of Partial Employment Protection Reforms: Evidence from Italy

Abstract We combine matched employer–employee data with firms’ financial records to study a 2001 Italian reform that lifted constraints on the employment of temporary contract workers while maintaining rigid employment protection regulations for employees hired under permanent contracts. Exploiting the staggered implementation of the reform across different collective bargaining agreements, we find that this policy change led to an increase in the share of temporary contracts but failed to raise employment. The reform had both winners and losers. Firms are the main winners as the reform was successful in decreasing labor costs, leading to higher profits. By contrast, young workers are the main losers since their earnings were substantially depressed following the policy change. Rent-sharing estimates show that temporary workers receive only two-thirds of the rents shared by firms with permanent workers, helping explain most of the labor costs and earnings reductions caused by the reform.

Определениe рациональных режимных параметров виброударного грохота

The paper deals with the problem of vibroimpact screen operating regimes selection for the regime of anti-phase oscillations of its elements. The criteria of effective screen operation accepted are impact impulse transferred to sieve from vibroimpact lattice and sieve double amplitude, which provide effective regimes of sieve cleaning from clogging particles and maintenance of preset sieve oscillations regime for support of technological parameters of material screening. Using the developed mathematical model and program the computational experiment was conducted for operating oscillation frequency range from 105 rad/s to 160 rad/s and adjustment gap between vibroimpact lattice and sieve for operating range from 0 to 5,5 mm. Influences of oscillation frequency and adjustment gap on impact impulse transferred to sieve and sieve double amplitude are obtained in the form of regression equations. On its base the recommendations for determining effective operating regimes of vibroimpact screen are offered. Obtained regularities explain nonlinear behavior of two-mass vibroimpulse oscillating system according to its operating parameters changing and show principal opportunities of system control. Also obtained results could find an application in screen operating regimes adjustment during commissioning and start-up operation and screen operation period, and could decrease labor costs and fasten the procedure of rational screen operating parameters searching.

What Do Employee Referral Programs Do? Measuring the Direct and Overall Effects of a Management Practice

Employee referral programs (ERPs) are randomly introduced in a grocery chain. On direct effects, larger referral bonuses increase referral quantity but decrease quality, though the increase in referrals from ERPs is modest. However, the overall effect of having an ERP is substantial, reducing attrition by 15% and significantly decreasing labor costs. This occurs, partly, because referrals stay longer than nonreferrals, but, mainly, from indirect effects: nonreferrals stay longer in treated than in control stores. The most supported mechanism for these indirect effects is workers value being involved in hiring. Attrition impacts are larger in higher performing stores and better local labor markets.

Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard

The design and engineering of piping and instrumentation diagrams (P&ID) is a very time-consuming and labor-intensive process. Although P&IDs show common patterns that could be reused during development, the drawing is usually created manually and built up from scratch for each process. The aim of this paper is to recognize these patterns with the help of artificial intelligence (AI) and to make them available for the development and the drawing process of P&IDs. In order to achieve this, P&ID data is made accessible for AI applications through the DEXPI format, which is a machine-readable, manufacturer-independent exchange standard for P&IDs. It is demonstrated how deep learning models trained with DEXPI P&ID data can support the engineering as well as drawing of P&IDs and therefore decrease labor time and costs. This is achieved by assisted prediction of equipment in P&IDs based on recurrent neural networks as well as consistency checks based on graph neural networks.

Assessment of design mechanical parameters and partial safety factors for Wire-and-Arc Additive Manufactured stainless steel

Early investigations suggest that the use of Additive Manufacturing (AM) technologies for construction has the potential to decrease labor costs, reduce material waste, and create customized complex geometries that are difficult to be manufactured using conventional construction techniques. Nevertheless, the full exploitation of AM technologies requires data on the material mechanical properties so that reliable and safety design requirements can be developed. Among different metal AM techniques, the so-called Wire-and-Arc Additive Manufacturing (WAAM) results to be potentially suitable to realize large-scale structural elements of any shape and size. However, the results of early experimental tests on WAAM-produced alloys suggest the need of ad-hoc considerations to properly interpret the geometrical and mechanical features of the printed outcomes.The present study analyzes the data obtained from the experimental results of tensile tests carried out on WAAM-produced 308LSi stainless steel elements with the purpose of calibrating design values and partial safety factors. In order to account for the anisotropic behavior proper of WAAM-produced elements, the design values of the main mechanical parameters have been calibrated for the three main orientations of the specimens with respect to the deposition layer. The calibrated design values and partial safety factors for the yielding and ultimate tensile strength are compared with recommended values for stainless steel structures as provided by EN1993:1-4 - Eurocode 3 (EC3). Additional considerations upon the Young’s modulus values, highly influenced by the anisotropic behavior of WAAM-produced stainless steel, are presented as well.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation.Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients.Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively).Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

Shoplifting in mobile checkout settings: cybercrime in retail stores

Purpose Retailers are implementing technology-enabled mobile checkout processes in their stores to improve service quality, decrease labor costs and gain operational efficiency. These new checkout processes have increased customer convenience primarily by providing them autonomy in sales transactions in that store employee interventions play a reduced role. However, this autonomy has the unintended consequence of altering the checks and balances inherent in a traditional employee-assisted checkout process. Retailers, already grappling with shoplifting, with an estimated annual cost of billions of dollars, fear that the problem may be exacerbated by mobile checkout and concomitant customer autonomy. The purpose of this paper is to understand the effect of mobile checkout processes in retail stores on cybercrime in the form of shoplifting enabled by a technology transformed the retail environment. Design/methodology/approach The authors conducted an online survey of a US sample recruited from a crowdsourced platform. The authors test a research model that aims to understand the factors that influence the intention to shoplift in three different mobile checkout settings − namely, smartphone checkout settings, store-provided mobile device checkout settings, and employee-assisted mobile checkout settings − and compare it with a traditional fixed location checkout setting. Findings The authors found that, in a smartphone checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs and peer influence had a stronger effect for prospective shoplifters when compared to experienced shoplifters; in a store-provided mobile devices checkout setting, experiential beliefs had a negative effect on shoplifters’ intention to shoplift and the effect was weaker for prospective shoplifters when compared to experienced shoplifters. The results also indicated that in an employee-assisted mobile checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs had a stronger effect for prospective shoplifters when compared to experienced shoplifters. Originality/value This study is the among the first, if not first, to examine shoplifters’ intention to shoplift in mobile checkout settings. We provide insights into how those who may not have considered shoplifting in less favorable criminogenic settings may change their behavior due to the autonomy provided by mobile checkout settings and also provide an understanding of the shoplifting intention for both prospective and experienced shoplifters in different mobile checkout settings.

Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems

The introduction of autonomous buses and taxis is expected to generate such benefits as cost reductions—and particularly for regional bus operations with a substantial deficit—as well as enhancing public transit accessibility through decreased trip costs. The purpose of this paper is to provide an overview of the impacts of introducing autonomous buses and taxis on metropolitan transportation systems by quantifying the costs of travel in Japan, and to discuss the potential benefits. First, this study sets the assumptions on autonomous driving technology, including its impacts on vehicle costs, the decreased labor costs for driving and safety monitoring in buses and taxis, and decreased driving stress for private car users. Next, operating costs are computed for autonomous buses and taxis in Japanese metropolitan areas. The costs of travel, or the sum of monetary and time costs, are then computed with and without vehicle automation for different trip types in high- and low-density metropolitan areas. The results highlight that the costs of public transit trips that currently have a smaller share of time costs in overall trip costs could decrease considerably due to vehicle automation. For instance, costs for 10–20-km trip lengths could decrease by 44–61% for taxi trips and 13–37% for rail/bus trips with taxi access, followed by a decrease of 6–11% for bus trips and 1–11% for rail trips with bus access. Further, private car trip costs could decrease by 11–16%. More substantial cost reductions in rail/bus trips with taxi access could occur in the case of smaller trip distances and/or in residential areas far from stations; larger reductions in rail trips with bus access could occur in low-density metropolitan areas. Finally, it is expected that vehicle automation in more fixed modes of public road transit could primarily benefit the transit industry and government, with such effects as improved labor productivity and reduced subsidies, while vehicle automation in more flexible modes could benefit metropolitan residents as well as the transit industry. This further suggests that a deficit of regional bus operations could be recovered during the transition to the full performance of autonomous buses.

Benchmarking low-cost inertial measurement units for indoor localisation and navigation of AGVs

In the last decade, the landscape of distribution and warehousing has drastically changed and is increasingly dominated by automated guided vehicles (AGVs) transporting palletised goods. The benefits that AGVs brought to the factory floor among which the decreasing labour costs, increasing safety, accuracy and productivity were also noticed by other sectors such as manufacturing, retail, e-commerce and, even military and healthcare. Nevertheless, the high initial investment and maintenance costs of these systems still remain bottlenecks for small and medium-sized enterprises (SMEs), slowing down their transition towards Industry 4.0. This demands for cost-effective AGVs equipped with low-cost exteroceptive and proprioceptive sensors for indoor navigation and localisation in highly dynamic environments. Therefore, this work presents the benchmarking of three low-cost and one medium-cost inertial measurement units (IMUs) characterised by an oft-recurring static analysis extended with a dynamic analysis. The former is based on the Overlapping Allan Variance (OAVAR) method and compares various sensor metrics such as white noise, bias offset and bias instability. The latter compares step and frequency response parameters of the IMUs, while being subjected to frequency ranges similar to those observed during AGV operation. In addition, these low and medium-cost IMUs are mounted on a mobile platform and applied in an indoor use case with a view to implement simultaneous localisation and mapping (SLAM) for AGVs in production sites. The influence of these IMUs on SLAM is investigated based on absolute and relative map metrics. Finally, it can be stated that with regard to AGV operation, the low-cost IMUs perform equally well compared to the medium-cost IMU.

Technical note: Evaluation of an automatic system for monitoring rumination time in weaning calves

Rumination time is a relevant marker for health evaluation. The use of rumination monitoring systems can decrease labor costs when measuring rumination time. The objectives of this study were to: i) validate the Hr-Tag rumination monitoring system for estimating rumination time in Zebu calves during the weaning period and ii) to evaluate the Hr-Tag capacity to identify differences in daily rumination patterns during the weaning period. Thirty-two Gyr calves averaging 69 ± 7.6 kg body weight and 77.0 ± 4.0 days were used. Calves were fed 4.7 ± 0.5 L/day of whole milk and ad libitum commercial textured starter ration up to 83 days old (pre-weaning period). During the weaning period (84–86 days old) the same starter was used, but corn silage was also available and only half the milk dose was offered. After weaning (87–92 days old, post-weaning period) only corn silage and starter were used. For the Hr-Tag system validation, the rumination time obtained by Hr-Tag was compared with visual observations at four 2 h intervals (0600 to 0800, 0800 to 1000, 1400 to 1600, and 1600 to 1800 h) at pre- and post-weaning. Two observers were used in all visual evaluations. To compare daily rumination pattern at pre-weaning, weaning and post-weaning, only the Hr-Tag data was used. Rumination time values between observers were highly correlated (ρ = 0.956; P < 0.001). Visual and Hr-Tag rumination time did not follow a similar probability distribution (P < 0.001), but they were correlated (ρ = 0.702; P < 0.001). The Hr-Tag system overestimated rumination time, mainly when the calves had visual rumination activity close to or equal to zero. The Hr-Tag detected differences in rumination time between weaning periods (P < 0.05). In conclusion, the Hr-Tag system overestimates rumination time in weaning calves, mainly by recording nonexistent rumination. However, the Hr-Tag system recorded rumination values correlated to visual observations and detected differences in rumination activity among weaning periods.

Determinants of trade balance in Polish and Czech manufacturing sectors

Research background: A strong industrial base is essential for achieving long-term sustainable economic growth and export competitiveness. In that sense, manufacturing remains a significant contributor to exports in the CEE countries. How-ever, its role and its influence vary between CEE economies and change over time.&#x0D; Purpose of the article: The main objective of this paper is to compare the determinants of the international competitiveness, measured by the net exports of the manufacturing sectors in the Czech and Polish economies, by using the database of 13 manufacturing sub-sectors in 1995-2011. The authors research the question of how much foreign and domestic demand, the level of labour costs, the level of sector innovation intensity, the level of sector openness to foreign markets as well as sectoral labour productivity influence the changes in trade balance.&#x0D; Methods: Our approach is based on employing an error correction model and SUR model to disaggregated sectoral manufacturing data.&#x0D; Findings &amp; Value added: The results of the analysis conducted show substantial differences in the roles particular variables play in explaining the net exports in individual sectors. For the majority of Polish and Czech manufacturing sub-sectors, generation of positive trade balance is determined by relative demand growth. An increasing labour productivity influences heavily a positive trade balance of Polish goods in majority of sub-sectors, however, a key factor in Czech sub-sectors is decreasing unit labour costs. The results of the analysis indicate mostly a greater impact of the researched factors on net exports in long rather than short term and the better capacity of the Czech economy to correct deviations from the equilibrium.

Applications of additive manufacturing in the construction industry – A forward-looking review

Abstract Additive manufacturing (AM), also known as 3D printing, fabricates components in a layerwise fashion directly from a digital file. Many of the early applications of AM technologies have been in the aerospace, automotive, and healthcare industries. Building on the advances in AM in these industries, there are several experimental applications of AM in the construction sector. Early investigations suggest that use of AM technologies for construction have the potential to decrease labor costs, reduce material waste, and create customized complex geometries that are difficult to achieve using conventional construction techniques. However, these initial investigations do not cover the full range of potential applications for construction or exploit the rapidly maturing AM technologies for a variety of material types. This paper provides an up-to-date review of AM as it relates to the construction industry, identifies the trend of AM processes and materials being used, and discusses related methods of implementing AM and potential advancements in applications of AM. Examples of potential advancements include use of multi-materials (e.g., use of high-performance materials only in areas where they are needed), in-situ repair in locations that are difficult or dangerous for humans to access, disaster relief construction in areas with limited construction workforce and material resources, structural and non-structural elements with optimized topologies, and customized parts of high value. AM's future in the construction industry is promising, but interdisciplinary research is still needed to provide new materials, new processes, faster printing, quality assurance, and data on mechanical properties before AM can realize its full potential in infrastructure construction.